Traditional site selection for commercial real estate relies heavily on drive-time polygons and census demographics. You draw a 10-minute drive-time ring around a potential location, pull household income and population density, and make a go/no-go call. It works — but it misses a huge amount of context about how people actually move through that area. Origin-destination (OD) data adds the behavioral layer that drive-time geometry cannot: where visitors to comparable locations actually come from. The U.S. Census LEHD/LODES program is the canonical public-reference for commute-flow analysis; the commercial equivalent, built over POI polygons from GSDSI's Global Mobility & Location Data and POI & Geofencing products, gives CRE investors a trade area defined by actual behavior.
Key Takeaways
Drive-time rings are a legacy approach. OD data replaces geometry with behavior — real visitor origin patterns at Census block grain.
A competing center drawing 40% of traffic from a zip code 20 minutes away would be entirely outside a drive-time ring. OD data surfaces that draw; drive-times miss it.
Trade area shrink quarter-over-quarter is a leading indicator of tenant softness that typically shows up in sales reports 6+ months later.
Census LEHD/LODES provides the public-reference analogue; the commercial OD layer built over POI polygons extends it to retail and mixed-use.
Why Drive-Time Geometry Is a Legacy Approach
A drive-time ring encodes one assumption: that customer draw is a function of road distance. That's a reasonable proxy for some verticals (convenience retail) and a bad proxy for others (specialty retail, destination entertainment, auto dealers). OD data replaces the assumption with measurement — show me where visitors to the three closest comparable centers actually live. The USDOT BTS passenger origin-destination data demonstrates the same principle at the national transportation grain, and the FHWA freight origin-destination program applies it to goods movement. The underlying point: trade areas are discovered by observation, not drawn by ruler.
The Practical OD Workflow for Site Shortlisting
The standard workflow at institutional CRE shops now looks like this:
Identify a shortlist of candidate sites and map their closest 3–5 comparable operating locations in each market.
Pull OD data for each comparable — visitor-origin distribution by Census tract or zip code, over a 12–24 month stable window.
Overlay the shape on top of the candidate site's catchment. Which candidate site captures the broadest geographic draw? Which overlaps most with the target customer base? Which has the least cannibalization against existing portfolio properties?
Layer visit-frequency distributions to separate trade-area depth (loyal repeaters) from breadth (one-off visitors).
Beyond initial site selection, OD data is reshaping how investors underwrite ongoing performance. If a property's trade area is shrinking quarter over quarter, that's an early warning sign that often doesn't show up in tenant sales reports for another 6 months. Conversely, an expanding trade area suggests regional pull is strengthening — which supports a more aggressive leasing or expansion strategy. The signal is particularly valuable because it decouples property-level performance from company-level consolidated numbers; a REIT can spot which assets are drifting long before the quarterly earnings print rolls them into a portfolio average.
OD Analysis at the Investment Committee Level
Several institutional CRE investors now require OD analysis as a standard component of investment memoranda. It's moved from "nice-to-have research supplement" to required input alongside appraisals and financial models. An IC slide typically includes:
The candidate site's measured trade-area shape, built from comparable-location OD data.
Visit-origin distributions showing breadth vs. depth of draw.
Cannibalization analysis against existing portfolio properties within the region.
Trade-area stability over the last 24 months — is this market strengthening, stable, or drifting?
Comparable-location benchmark reads on visits-per-square-foot normalized against the measured trade area.
How is OD data constructed from raw mobility signals?
The input is device-level visit attribution at POI grain. OD data aggregates those visits by home/work Census block (derived from stable overnight and daytime device location patterns) and outputs a visitor-origin distribution per POI. Quality depends on polygon-based POI attribution, stable panel composition, and enough device density in each Census tract to avoid small-sample noise. The Census LEHD methodology is the canonical reference for how the commute-origin inference should work.
What sample size do you need for OD data to be reliable at Census-tract grain?
Depends on the POI — a single QSR with 500 visits/month produces a noisy OD read; a shopping center with 50,000 visits/month produces a clean one. Practical guidance: require ≥2,000 attributed visits over the analysis window to produce a Census-tract-level origin distribution; use zip-code grain for smaller-volume POIs.
Can OD data detect cannibalization between portfolio properties?
Yes — by identifying the overlap in home-origin Census tracts between two properties, you can estimate the share of traffic each property is pulling from the same household base. If a new property acquisition's measured OD shape overlaps 40%+ with an existing portfolio property, cannibalization risk is material and should be explicitly modeled in the underwriting.
How stable are OD reads over time?
In stable markets, OD shapes are typically consistent quarter-over-quarter — the same zip codes show up as primary sources. Material shifts usually indicate something real: a new competitor opened nearby, a highway project changed access patterns, or a demographic shift is underway. The stability is exactly why trade-area shrink quarter-over-quarter is a defensible leading indicator.