Amazon has officially announced the two cities that will host their new headquarters. What do we know about the location factors that went into their decision?
Back in 2017, we discussed Amazon’s acquisition of Whole Foods and what we could learn about their site selection model using location intelligence. With Amazon officially announcing Long Island City, NY and Crystal City, VA as their new secondary headquarters locations, let’s take a look at what factors went into their decision, and how the site-selection process can be streamlined using LandVision™.
Amazon HQ2 Site Selection Criteria
In September 2017, Amazon submitted an eight-page public request for proposal (RFP) detailing their requirements for potential headquarters locations. Thanks to this, we actually know a lot about what the e-commerce giant was seeking from a new host city.
As you can imagine, Amazon had very strict site selection requirements. The company sought existing buildings on site that were at least 500,000 square feet in size and situated on up to 8 million square feet of land. Additionally, the site needed to be located within 30 minutes of a population center and no more than 45 minutes from an international airport. This last point alone helped narrow their search right away.
Amazon is in the logistics business, so it’s no surprise that their expectations were high in this area as well. Onsite access to mass transit, whether it be train, subway, or bus, was mandatory. The site could be no further than two miles from major highways, and Amazon also requested a full transit analysis that included traffic congestion during peak commuting hours. The company needed to ensure that logistics were accounted for up front so that any factors impacting their operations could be minimized.
The community around a prospective site was important to Amazon, both from a workforce perspective and in terms of livability. Sites located in close proximity to a “strong university system” were preferred, and the company also asked applicants to provide information on the cost of living, housing prices/availability, and crime rates. What this tells us is that the company clearly wanted to be in an environment conducive to attracting top talent, but they also wanted to find a place that their employees would want to live in.
How LandVision™ Helps With Site Selection
Using our powerful map-based application, LandVision™, it’s possible to analyze these criteria, and any other important factors in the site selection process, spatially. Our nationwide parcel database, complete with full ownership information, property value, and much more, can be accessed with just the click of a button. Demographic information, such as crime rates and household income, administrative boundaries, and even traffic congestion data are all available out of the box. Additionally, any compatible data can be imported and visualized alongside our content. This puts all of the information needed to make site selection decisions in one standard location for easy analysis.
LandVision™ allows you to visualize only the properties that are best suited for your needs. For example, Amazon would have the ability to search within an area for all properties that held buildings larger than 500,000 square feet, with specific parameters around the lot size as well. In the figure above, we’ve used LandVision™’s aggregate acreage search capabilities to highlight all properties, or groups of adjacent properties owned by the same entity, that are between 150 and 200 acres in size in Arlington, VA. This ensures that Amazon is aware of all of the properties in the area that may be suited for their new headquarters, even if they may need to lease multiple properties to accommodate their facility.
With a preliminary list of sites outlined, Amazon would be able to begin layering information on top of prospective sites to ensure that their other criteria were met. As an example, annual traffic data could be overlaid to simultaneously ensure that a site is close enough to major highways and understand patterns of congestion, immediately satisfying two requirements. The company could even have searched by average annual daily traffic count to eliminate sites near heavily congested roads.
As they completed the process of eliminating sites that failed to meet basic logistics criteria, Amazon could then turn their attention to the greater surrounding area. The quality of the local education system was another big factor in Amazon’s decision, which could have been added to their map as a layer. At a glance, Amazon would be able to see the school districts that governed local public schools, and they could even drill down to what parts of town were assigned to specific elementary, middle, and high schools. They could even view the ratings of the school districts! Additional demographic layers would also help provide a more complete picture of the nearby population.
For mapping proximity to major universities, Amazon would benefit from LandVision™’s easy configurability. University location data is publicly accessible, and by simply uploading a spreadsheet or shapefile into LandVision™, Amazon could have visualized these locations.
As they approached the time to select finalists, Amazon could utilize LandVision™ to generate site profile reports for any promising properties. For any property under consideration, these reports would provide imagery, property information (including ownership), area demographics, crime data, and much more, within a specified range of a property.
With the help of a location intelligence platform, site selection processes of all shapes and sizes are made much more manageable. Are you interested in learning how LandVision™ can help make your site selection process easier? Schedule a demo to see LandVision™ in action, or contact us today for more information!