When property data sources disagree, it's frustrating at best, and at worst it burns dozens of hours on research or sends resources after a site that was never really available. Conflicting records are a normal part of site selection, and most selectors already have plenty of data to work with.
Few teams have an agreed method for deciding which record wins, and the gap costs them time and sometimes the deal. Each conflict gets settled by whoever happens to be doing the research, so the calls vary from analyst to analyst and some numbers in the final recommendation stay open to debate.
Anyone who has screened a retail site has run into some version of this. An end-cap in a neighborhood strip center shows one square footage on a listing portal and another on the broker's flyer, and two sites still advertise it for lease weeks after another tenant signed an LOI. Before the space makes a shortlist, someone has to pick up the phone and sort out which version is right.
The record to trust is the verified one, meaning the version closest to its primary source and most recently confirmed, as long as it has enough detail for the decision at hand. A data point becomes usable once your team can say where it came from and when it was last checked, and with those answers on hand, the end-cap either moves to the shortlist or comes off the list the same day.
Why Do Property Data Sources Disagree So Often?
Start with something as basic as a submarket's borders. One data portal may define a submarket by ZIP code, while the brokerage report on your desk may use custom borders its research team drew years ago. No wonder vacancy rates and lease comps for the same submarket vary from one provider to the next, as a 2026 GrowthFactor guide points out.
Square footage measurements cause the same kind of trouble, and comparing a landlord's rentable figure with the county assessor's gross number for the same strip center can show a gap wide enough to change the rent math.
Sources also update on different schedules, and retail space can change hands fast. A listing broker might mark the end-cap as pending the afternoon the LOI comes in, but a portal pulling from the broker may not refresh for a week or two, and anyone searching in the meantime still sees it as available.
Some conflicts come from records that never get matched to each other. A strip center filed under the landlord's LLC in one system and under its street address in another can look like two separate properties, and skipping identity resolution means that same property showing up as three different parcels across three systems. You can compare those records side by side and never realize you're looking at one building.
Syndication copies whatever the original feed contains, so a stale status or a typo in the square footage can spread across a long list of portals and aggregator sites within days. When five sites show the same number, check whether they all pulled it from one feed before treating it as confirmed.
Conflicting Property Data Raises the Stakes in Site Selection
The International Trade Administration (ITA) describes the research phase of site selection as essentially a process of elimination, with teams using data to narrow a long list of locations down to a few. Each round of cuts happens quickly and relies on whatever records the team has at hand.
Picture this: a QSR team screens a pad site out of consideration because the portal shows 0.8 acres, below their minimum for drive-thru stacking. The broker's flyer puts it at 1.1 acres, but the analyst never opens that flyer. The site never comes back up for consideration, and the team may never know they passed on a viable location.
Revisiting a cut takes time most teams don't have. About 67% of site selectors surveyed for the Site Selectors Guild's 2026 Pulse Check said they had compromised on product, regulatory, or incentive policies to guarantee speed to market on recent projects, and teams willing to make those trades are unlikely to circle back and double-check an acreage figure.
An AI screening tool working from the portal record would have made the same cut in seconds, with no analyst around to second-guess it. Pre-RFI screening that once took weeks now takes hours, according to Devin Hillsdon-Smith of Hyphen Strategies, who also cautions these systems "can misattribute data, conflate adjacent geographies, or weight recent negative sentiment more heavily than a balanced evaluation warrants."
Verified Property Records Should Win When Sources Conflict
Checking where a record came from is only half the job, because the date matters just as much. A broker flyer from last spring can be less reliable than a portal record updated yesterday from the same broker's feed.
Public data needs extra caution, since the ITA notes some public sources have lags of 24 to 48 months. Those datasets are useful for labor and demographic context, but they won't show whether the end-cap leased last week.
When two records conflict, check the following before choosing one:
- Did the record come from the owner or listing broker, or from a site that copied the listing?
- When was it last confirmed, and by whom?
- Does it answer the specific question at this stage, such as whether a pad site's zoning allows a drive-thru?
- Does it match what the team has already confirmed firsthand, like a pad measured on a site visit?
- If a figure can't be verified yet, is it flagged as unverified?
Answering those questions quickly is the hard part, because confirming each record by phone is time consuming for even a handful of sites but totally unreasonable across a long list.
What Makes Property Data Defensible in a Site Recommendation?
Once a site reaches the shortlist, the real estate committee will question every number in the recommendation memo. A number is defensible when your team can name its source and confirmation date and explain why it was chosen over the other versions.
Preparing for those questions is why the research phase takes so long, since every figure in the memo needs an answer behind it. The time is worth it, because one unanswered question can make the committee doubt the whole recommendation.
If the committee asks where the $38 PSF asking rent came from and nobody can say, it has no reason to trust the occupancy cost or sales forecast built on it, and the site may go back for another round of research while the opening date slips. If the listing said the pad allows a drive-thru and the zoning turns out to require a special use permit, the company may already have paid for legal review and site engineering on a location that can't operate the way the brand needs.
When a number can't be verified before the recommendation is due, flag it as unverified. Showing the asking rent as $34 to $38 PSF, with the source of each figure, tells the committee exactly what still needs checking, and the committee can move the site forward as long as the rent is confirmed before the LOI.
Verified Data Shortens the Research Phase
When an expansion team starts with listings already verified against the broker or owner, the hours spent confirming sources and dates go to comparing sites, and fewer numbers turn out wrong during diligence.
Resimplifi was built to do that verification work up front. It collects listings directly from more than 8,000 primary sources and checks them daily, so each property has one current record with up to 120 data fields. The database covers more than 2 million sale and lease listings nationwide.
Teams using AI tools for early screening can query the same verified data through Resimplifi Agent, which works with Claude, ChatGPT, Perplexity, and other MCP-compatible tools. The AI tool then returns figures with a source and verification date behind them.
The expansion teams that move fastest can tell quickly which version of a record is verified and spend their research time on the sites themselves. Starting from verified records cuts the hours spent calling brokers and checking dates, and it gives the real estate committee a clear source for every number in the memo.
See how fast your team can find verified listings in your target markets. Book a demo with Resimplifi.