The power of Tensorflight’s AI and machine learning algorithm makes it simple to evaluate any building in the world.

But what about buildings that aren’t really part of this world?

To show the effectiveness of our product, we decided to analyze Barcliff Building A of Emory University, better known as the Hawkins National Laboratory, a real, yet fictional place from the Netflix show Stranger Things.

The building has been the filming location for many feature films over the years, owing to it’s intimidating presence and unique use of materials. Is such a unique design difficult for Tensorflight to analyze?

Not at all.

All that was needed was an address to gather hundreds of data points on the building in mere seconds. All this information was captured remotely, without having to have anything or anyone in the vicinity of the building.

Hawkins National Laboratory Data & Valuation

Here’s some of the data that Tensorflight pulled from the building:

Number of stories: 5 (+basement)

Footprint area (ft^2): 55018

Estimated floor area (ft^2): 174543

Building occupancy type: Hospital

Occupancy types: Health Care Services

RMS occupancy type: 8000

Construction type: Reinforced concrete

RMS construction type: 3A

Year built: 1965

Roof pitch: Flat

Roof geometry: Flat

Roof material: Bituminous waterproofing

Facade material: Concrete

Roof condition: Fair

Has basement: Yes

Quality of finishes: Medium

Number of trees within 25 meters from the building: 22

Flood zone: X

Distance to coast [mi]: 223.0

Estimated building replacement cost: $82,357,000

Within the Tensorflight suite, a detailed image is shown, with a breakdown of areas, materials and construction types. The suite also presents the age of the building, construction types and occupancy types.

The information generated by Tensorflight is then used by insurers, underwriters and other analysts to create more accurate and relevant insurance policies with real risk factors taken into account.

The Hawkins Laboratory, while looking ominous and foreboding, is easily analyzed by our tool. If you’d like to put Tensorflight to work for any other address, reach out to us and see how we can make this technology work for you.