At the core of every home sale, mortgage, refinancing or property assessment are boundary lines.
Most obviously, parcel boundaries are needed to create maps and see the location and shape of a property. But boundary data is crucial for compiling key data on individual properties and putting that data into context.
Boundaries are all important for property data aggregation. To yield precise and accurate intelligence, a property’s key features, like the square footage and lot size, need to be put into a hyperlocal context. Comparing properties to citywide averages is often misleading.
It is best to compare one home to similar homes near it; homes served by the same schools, located near the same shops and affected by the same crime rates, flood risks and other external factors.
But to produce hyperlocal data for a property, you first need to draw accurate boundaries from the building footprint and parcel boundary outwards.
Just a few uses of boundary lines include:
- Showing the size and shape of a building footprint within a parcel boundary;
- Displaying all the homes for sale within a subdivision or neighborhood boundary;
- Showing nearby schools and key community services and shops within walking distance of the home;
- Displaying the boundaries of a federally designated flood zone located on or near the parcel boundary line.
Property data is intended to shine light and eliminate human error and bias. That could be to estimate a home’s value by comparing nearby properties that were recently sold, or it could be to show the property’s external risks from crime and natural disasters. But without accurate and clearly defined boundaries lines, the data can get muddy.
Automated valuation models, for example, use data inputs to assess the value of a property. But without including hyperlocal data on the neighborhood and schools that affect a property’s value, the AVM’s modeling likely won’t produce accurate assessments.
What Stands Out About ATTOM’s Boundary Data
There are two broad categories of property boundaries. The first is at the property level, building footprints and parcel boundaries. The second is at the level of a census tract, subdivision, neighborhood and outward at the citywide, county and regional levels.
While numerous companies are aggregating property data, the data isn’t of the same quality. The same is true of boundaries.
ATTOM’s solutions are based on a belief that property data needs to be hyperlocal, and that data can only be hyperlocal by drawing accurate boundaries. The company has invested heavily in its boundary lines. Its property boundaries are drawn and regularly updated by an in-house team of geographic information system (GIS) experts.
At the property level, ATTOM has more than 155 million parcel boundaries in the U.S., representing roughly 99% of the population.
A parcel boundary is updated on average every six months and at least once a year by the GIS team. And this is important because parcels can be split, combined or modified.
The most common use of a parcel boundary is for mapping, showing the parcel size and dimensions in relation to other homes, businesses and schools. But once defined, a parcel boundary can also be linked to a huge amount of property information from the county assessor and recorder, foreclosure data, building permits and other sources.
The parcel boundaries can be used in tandem with other maps; for example, FEMA’s flood zone maps to determine whether any part of the parcel falls within a flood zone that would require the owner to obtain flood insurance to qualify for a mortgage.
Putting US Parcel Boundaries in Context
What separates property data aggregation companies is how well they can compare a property to other properties located near it.
To do that accurately, you need data that compares properties within the same neighborhoods.
There are four levels of neighborhoods: macro-neighborhoods, like lower Manhattan; defined neighborhoods like Tribeca or the East Village; sub-neighborhoods within neighborhoods and subdivisions. ATTOM believes that individual properties are best compared to properties located within an area no larger than a neighborhood.
When aggregating real estate data, the boundaries of a zip code are often used as the smallest segmented area; however, zip codes are often too large to yield hyperlocal insights. Several neighborhoods can be in the same zip code. Some of those neighborhoods will be more desirable than others. The home values and other key data points that affect value, such as the crime rates and the schools, can vary across a zip code.
But it is not always clear how to draw a neighborhood boundary or label a neighborhood correctly. City locals may know a neighborhood by one name while the federal and state government may label it with another.
To capture these nuances, ATTOM has drawn on several sources to define neighborhood boundaries. In total, the company’s GIS team has drawn 750,000 boundaries in more than 600 metro areas and 10,000 cities and towns across the U.S. and some in Canada.
The names and boundaries of the neighborhoods reflect the consensus of locals and have been verified via media accounts, websites and tourism offices.
Drawing Hyperlocal School Boundaries
Schools are another important boundary that affects home values. However, just as a zip code is often too large to yield hyperlocal insights, so too can be the boundary lines of a school district.
ATTOM has two levels of school boundaries: one for school districts and one for school service areas. A school district, however, can contain several schools, each school covering certain neighborhoods, each with different ratings and potential impacts on the value of that home.
The company has also drawn service areas of bilingual and nonbilingual schools, which is especially useful data for real estate professionals selling homes near the southern border.
Hyperlocal school boundaries and parcel boundaries have many practical uses. Boundaries are needed to produce accurate maps, do market reports and risk assessments, and produce marketing materials.
ATTOM’s parcel boundaries are the most extensive in the market. By closely drawing and regularly updating boundary lines from the building footprint outwards, ATTOM delivers the most reliable hyperlocal data in the marketplace.
Written by: Jennifer Von Pohlmann