Most commercial real estate companies will tell you they use data. They review market reports, use CoStar, and track vacancy rates and absorption trends across the submarkets they cover. Data is part of the standard toolkit for any serious operator.
Dalfen Industrial’s data operation is different in kind, not just in degree. The company has built a proprietary analytics infrastructure specifically designed to identify optimal last-mile locations down to the zip code before the market has priced in what those locations are worth. It has data analytics teams working in India, a CTO based in Dallas overseeing the technical architecture, and a scoring system for evaluating locations that has been continuously refined since 2015. When Dalfen Industrial targets a building in a specific part of a specific city, the decision isn’t based on a market report. It’s based on a model the company spent years developing.
The 2015 Scoring System and What It Measures
The last-mile scoring system Dalfen Industrial built in 2015, with external consultants, addresses a question that sounds straightforward but is genuinely difficult to answer with precision: is a given location more valuable to a tenant than the alternatives in the same market?
This is different from asking whether a location is good. Good is a relative judgment, and in real estate, it usually measures against the general market rather than against specific competing properties. Dalfen Industrial’s scoring system is more granular. It evaluates whether a specific location allows a tenant to reduce transport and/or employment costs relative to what that tenant would experience in the nearest competing buildings.
The reason transport costs matter so much in this analysis is that they represent 45 to 75 percent of a typical supply chain budget. Real estate costs, by comparison, represent 3 to 6 percent. When a building’s location genuinely reduces transport costs, the economic case for paying a rent premium is compelling and quantifiable. The tenant is trading a small increase in one cost category for a large reduction in a more impactful one. The scoring system tries to identify, property by property, which locations create that trade-off most favorably.
Sean Dalfen describes the output of the scoring process as a set of target locations within a given market: specific zip codes, or parts of zip codes, where the combination of proximity to population, logistics infrastructure, and workforce concentration makes a property genuinely more valuable than alternatives that might appear similar on paper.
Why Zip-Code-Level Precision Matters
The difference between a property that scores well on Dalfen Industrial’s methodology and one that looks similar but doesn’t can be subtle from the outside. Two buildings in the same industrial park, a quarter mile apart, might have materially different access to the highway system, the workforce, or the end customers being served. One might sit at the intersection of two logistics routes that cut delivery times by 20 percent. The other might require an extra turn that costs 15 minutes per route and compounds across hundreds of deliveries per week.
These differences don’t show up in a standard market analysis. They show up in the data when you’re comparing specific buildings against specific tenant operating requirements at the zip-code level. That’s the work Dalfen Industrial’s analytics teams do. Not market-level analysis, which every operator can access, but property-level analysis that requires the kind of proprietary infrastructure the company has built over the years.
The practical output is that Dalfen Industrial shows up at buildings before they’re available, with a clear view of what those buildings are worth and why. Sellers who receive an approach from the company often find that the offer reflects a more detailed understanding of the property’s value than they expected from a buyer who hasn’t toured the building or reviewed the leases.
The CTO in Dallas and the Team in India
The staffing model behind the data operation reflects Dalfen Industrial’s serious commitment to the analytical side of its business.
Keats Ali, Dalfen’s CTO is based in Dallas, which is where Dalfen Industrial is headquartered, and works alongside the investment and acquisitions teams rather than operating as a separate function. The proximity matters: the analytics work isn’t being done in isolation from the deal-making. It informs it in real time.
The data analytics team in Dallas and India handle the volume of work required to maintain zip-code-level coverage across the markets Dalfen Industrial operates in, which span most major metro areas across the U.S. Maintaining meaningful analytical depth across that geographic footprint requires resources that a conventional acquisition team couldn’t sustain.
The combination of local market expertise, through boots-on-the-ground teams in each market, and centralized analytical infrastructure enables Dalfen Industrial to operate at the level of granularity that makes the off-market strategy work. You can’t pursue individual buildings based on zip-code-level scoring without the ability to accurately and efficiently score those zip codes at scale.
From Zip Code to Building to Closed Deal
The process Dalfen Industrial follows after identifying a target location illustrates how the data operation connects to the actual business of acquiring real estate. Once the analytics teams identify a zip code or cluster of zip codes that meet the scoring criteria, the boots-on-the-ground team in that market identifies which specific buildings within those locations meet Dalfen Industrial’s property criteria: modern construction, appropriate suite sizes, strong parking ratios, and dock-high loading.
The target list that comes out of that process is a set of specific buildings, identified by address, owned by specific sellers, with whom Dalfen Industrial will begin building or deepening a relationship. The company isn’t waiting for those buildings to come to market. It’s approaching owners directly, making clear that there’s interest, and maintaining that relationship over time until the seller is ready to move.
On Dalfen Industrial’s most recent fund, 82 percent of deals came through this process or variations of it. The off-market rate isn’t a lucky streak. It’s the output of a system designed to identify the right buildings and reach their owners before a broker is involved, at which point the conversation becomes competitive and the advantage of knowing exactly what the asset is worth and why diminishes.
What This Infrastructure Makes Possible
The data operation behind Dalfen Industrial’s portfolio is not, ultimately, a technology story. It’s a competitive strategy. The analytical infrastructure, the last-mile scoring system and their property-level conviction score (which uses AI to score properties based on tens of thousands of comps dating back over a decade) are all continuously refined. Dalfen uses all of this data to answer one question more accurately than anyone else in the market can. Which buildings, in which specific locations, are worth more than the market currently believes?
When the answer to that question is reliable and repeatable, and when the company has the relationships and the reputation to close on those buildings without a competitive process, the result is a portfolio assembled from assets that were undervalued at the time of acquisition. Not undervalued because the market was irrational, but undervalued because most buyers were working from market-level analysis while Dalfen Industrial was working from zip-code-level data and proprietary conviction scoring.
That gap in analytical precision, sustained over two decades and across more than 60 million square feet of infill industrial space, is what the data operation is built to protect.
