There's a cost that doesn't appear on any balance sheet, isn't tracked in any quarterly report, and rarely comes up in budget conversations. It's paid in staff hours that disappear into correction and verification tasks, in deals that dissolve before they're ever logged, and in relationships with brokers and site selectors that gradually fade.
This is the bad data tax. Every organization working with commercial real estate listings is paying some version of it, whether they know it or not.
Sadly, most of the organizations paying the bad data tax believe their numbers are mostly fine. Functional, if not perfect, and good enough to work with. That assumption carries more pain than meets the eye.
Gartner research puts the average annual cost of poor data quality at $12.9 million per organization. IBM has long estimated that bad data costs the U.S. economy roughly $3.1 trillion a year, a figure still widely cited in 2025 and 2026 analyses.
These are enterprise-scale numbers, but the principle applies at every level of the market: inaccurate data is an active, ongoing operating expense that can, if left unfixed, worsen over time.
The Real Cost of Bad Data
"When we audited our support team's time, we found they were spending more time cleaning up stale listings and inaccurate data than actually helping customers. I immediately knew we needed to build a solution because bad data wasn't just a problem, it was a line item we'd never bothered to name."
Staff Time and Operational Drag
The most immediate cost of inaccurate listing data is the labor it wastes. When listing availability statuses are wrong, specs are missing, or the same property shows conflicting information across platforms, someone on your team has to sort it out. That person was probably hired to do something else.
Research from 2025 suggests that knowledge workers already lose close to a quarter of their workweek navigating information that's hard to find, inconsistently formatted, or simply wrong. In listing data, that pattern repeats in predictable ways across organizations of every size.
Common data rework scenarios include:
- A listed property was leased weeks ago, and a staff member now has to trace the error across every platform where it appears.
- Conflicting specs exist for the same property on two different platforms, requiring someone to identify the authoritative source and propagate the correction.
- A site selector or broker requests rapid verification of a listing, and the team can't confirm it without a manual check.
- A report goes out with outdated figures, triggering a correction cycle across internal materials and client-facing outputs.
The Compounding Effect
In organizations managing hundreds or thousands of listings, these scenarios leave messy trails and problems compound. A single stale listing generates downstream corrections across internal reports, client-facing materials, and every platform where that property appears. One bad record has now created multiple correction tasks, and that cycle repeats every time a new error enters the system.
Deal Flow Interruption
Inaccurate data costs time, which costs transactions.
A broker who brings a client to a property that turns out to be unavailable moves on, not just from that listing but from the source that surfaced it. A site selector who can't verify a critical spec typically won't ask for clarification, choosing instead to eliminate the market and continue the search elsewhere.
This cost to deal flow is hard to account for, as you'll almost never know when it happens. A prospect who encounters bad data doesn't file a complaint or send a correction; they simply stop calling. The absence of that inquiry looks no different from a slow market.
There's no feedback loop built into that failure. The same bad listing continues to fail for every new prospect who encounters it until someone manually corrects it, meaning the cost recurs and scales, tied to listing volume and how long errors persist.
This matters more now than it did two years ago. While sales slowed in the high-interest environment of 2023-2024, Altus Group's national transaction data showed $179.9 billion in quarterly deal volume in Q4 2025, up more than 20% from both the prior quarter and the year before. For 2025 overall, transaction volume climbed to $560 billion, a 14% year-over-year increase and the first annual rise in property counts since 2021.
In other words: the market is moving again. Principals are most likely to transact where their decisions can be supported by the most accurate data and listings. Therefore, a bad listing in a constrained pipeline is a material liability.
"Having accurate data is critical, but how you present it matters just as much. View Pro allows us to deliver both by providing real-time, easy-to-use property information that today's site selectors expect, while positioning our community as organized, responsive, and ready for investment."
Relationship and Reputational Erosion
The longest-lasting cost of bad data is also the least visible. Brokers, site selectors, and investors who encounter unreliable listing sources simply stop relying on that source. The organization never receives a signal that anything has gone wrong, which is how it disappears from the consideration set of the people who matter most.
Data-strategy research describes this as a trust erosion curve. Small quality issues accumulate gradually until a visible failure causes a steep, sudden drop in credibility. CDO Magazine analysis of this pattern applies directly here: rebuilding trust after repeated data quality failures takes far longer than preventing them in the first place.
An organization that is unknown can become known through consistent, accurate data. An organization that is known as unreliable faces a much more difficult journey, because reputational credibility has to be rebuilt against an established expectation rather than earned on a blank slate.
The erosion isn't linear. A broker who encounters one stale listing may give a source a second chance. After three, they move on. That progression from trust to disengagement happens without the organization ever noticing the loss.
A 2025 Salesforce survey found that 76% of business leaders feel increasing pressure to base decisions on data, but many admit they don't fully trust or understand the numbers they're working with. That skepticism extends to external data sources, and listing platforms that have earned a reputation for accuracy gain measurable engagement advantages as decision-makers grow more scrutinizing of their inputs.
In CRE contexts, those numbers, good or bad, translate directly into broker relationships and long-term market credibility.
Missed Opportunities and Reporting Exposure
For economic development organizations, civic agencies, and state programs, listing data underpins grants, economic impact reporting, and submissions to federal agencies. When the underlying data is inaccurate, those submissions are inaccurate too.
This cost is less visible than a lost deal but carries different consequences. A compromised report, an unsupportable grant claim, or a data discrepancy that surfaces during an audit creates credibility problems with stakeholders who have no direct view into your data pipeline and no particular reason to extend the benefit of the doubt.
A 2025 global study found that 58% of business leaders acknowledge their organizations make key decisions based on inaccurate or inconsistent data most of the time. In economic development contexts, that exposure impacts both internal strategy and the external record that funders, regulators, and partner agencies rely on.
Bad Data Gets Worse
Every line of the bad data tax shares one characteristic: it gets worse over time if left unaddressed.
Data isn't a static asset, and requires maintenance to stay useful. According to recent industry research, B2B data decays at roughly 22.5% per year, and in high-turnover markets that rate can reach 70%. The decay can vary across data types, because different fields degrade at different rates and for different reasons:
- Availability status changes the moment a lease is signed or a sale closes, often without any corresponding update to the listing.
- Contact information shifts as brokers change firms or coverage areas.
- Physical specs change after renovations, expansions, or rezoning.
- Pricing and lease terms move with market conditions and review cycles.
AI Amplifies Bad Data
The compounding effect matters especially now, as organizations onboard AI tools for market analysis, reporting, and prospect matching.
AI doesn't know to check for inaccurate inputs and can quickly scale them. A stale listing or a missing spec that would have misled one person can now mislead an entire automated workflow, compounding the same error across every output the system produces.
The underlying accuracy problem in CRE data compounds this further. Real estate database accuracy across major providers averages around 75%, with roughly 30% of availability and contact data going stale annually without active refresh cycles.
An organization that isn't actively maintaining its data is losing ground fast. Below a certain accuracy threshold, the problem becomes actively counterproductive. Data that confidently points in the wrong direction redirects resources, erodes trust, and produces cascading corrections that cost more than a timely update would have.
Why Most Solutions Don't Solve The Bad Data Problem
Many organizations that recognize the bad data problem reach for a solution and find one that doesn't fully work. The market has no shortage of platforms that promise to aggregate, clean, or surface better listing data. Most of them fall short for the same structural reason.
Front-end solutions are designed for a pleasant search experience, interface, and output. What they don't address is what happens to data between the moment it enters the system and the moment someone acts on it.
Aggregation without verification produces confident-looking bad data. That's operationally more dangerous than obvious incompleteness because it doesn't flag its own unreliability.
What matters most is where verification happens in the system: whether it's built into the backend as a continuous process, or applied to the front end as a periodic cleanup exercise. Organizations that conflate the two end up investing in platforms that appear more reliable without actually becoming more reliable, which means the bad data tax continues accruing behind a pretty interface.
Questions to Ask Any Data Platform
Before relying on a listing data platform for broker engagement, site selector outreach, or reporting, it's worth asking:
- How often are source records reviewed for accuracy, not just aggregated?
- When a property's availability status changes, how quickly does that change appear across the platform?
- Is verification manual, automated, or a combination, and can it be independently validated?
- What happens when a broker reports a discrepancy?
- Can the platform distinguish between a listing that has been verified and one that has been only syndicated?
"Most data platforms treat verification like a feature you add at the end. We treat it as part of the foundation from the start. That distinction sounds small, but it shows up operationally because your team is either spending hours fixing listings or actually using them."
Resimplifi's Verification Model
A platform built around backend verification can answer these questions with specificity. One built around frontend aggregation typically cannot, and that lack of response is worth taking seriously.
Resimplifi's model is built on this distinction. Drawing from 3,000+ verified sources reviewed weekly across 470,000+ listings, verification operates in the background as the core function of the platform rather than a periodic cleanup layer. Local broker knowledge that would otherwise stay siloed gets captured and reconciled at scale, so the data a selector or broker encounters reflects what's actually available, to the spec that's actually accurate.
Stop Paying the Tax
Every organization working with commercial real estate data is paying some version of the bad data tax. But does your organization know what it costs and have you decided to what extent that cost is acceptable?
The organizations that most effectively reduce it treat data quality as infrastructure. They invest in well-designed and well-maintained data plumbing that functions in the background, maintaining accuracy continuously rather than recovering from it episode by episode.
The returns of healthy data maintenance compound positively nearly as much as bad data compounds negatively. Accurate market profiles become more trusted, more discoverable, and more defensible over time. That reputation shows up in growing broker engagement, site selector shortlists, grant submissions that hold up under scrutiny, and deals that come from markets known to be reliable.
If you're not sure what your listings are actually costing you, that's a good place to start.
Talk to the Resimplifi team. Book a demo