For years, real estate has been described as a data-rich industry. But in practice, most organizations still struggle to collect, trust, and use their data at scale.
Across multiple episodes of the Innovation Blueprint podcast, founders, CEOs, and operators repeatedly came back to the same conclusion: the real challenge in real estate isn’t analytics or AI — it’s data foundations. Fragmentation, inconsistent structures, and lack of trust prevent teams from turning information into action.
Here’s what industry leaders are saying about real estate data — directly, in their own words.
Data Fragmentation Is the Root Problem
Joe Stockton, Co-Founder & CEO of Oyster Data described a reality many operators recognize immediately:
“We never had good data on the equipment, the work order histories, the warranties, insurance policies… We were constantly paying for the same work twice… every time we were selling a portfolio, getting these property reports together was a whole hassle.”
Data in real estate often lives across spreadsheets, PDFs, PMS platforms, vendor systems, and inboxes — with no single source of truth.
Joe explained what happens when that fragmentation is addressed:
“What we’ve done with Oyster is bring together all of this fragmented and siloed data into one spot — a robust, cleansed system of record… once that system of record is structured, it becomes actionable.”
Benjamin Allen, the Co-Founder of GreenLite, highlighted that this issue extends beyond owners and operators into the broader ecosystem:
“There’s not a single platform of record on the municipality side… it’s fragmented… and there’s change management… folks in construction and real estate have done it one way for a long time.”
Data Collection at Scale Is Still a Manual Burden
Without structured systems, even basic questions become expensive.
Joe Stockton shared how much time teams lose simply trying to locate information:
“Some customers have asset managers spending 20–30% of their time just trying to track down very fundamental questions about their portfolios… because they don’t have a directory of all their inventory at the equipment level.”
At scale, manual processes simply break. Alberto Quiroz, President of Intellimeter Canada Inc., put it bluntly:
“You got about 200 million data points and you want to put them into Excel… But if you had an artificial intelligence engine to take all that data, clean it, and really look at the outliers… instead of doing it manually — because it’s massive.”
This is where automation becomes less about convenience and more about feasibility.
Data Quality Still Matters More Than Any Model
Multiple leaders emphasized that poor data quality undermines even the most advanced analytics.
Michael Broder, CEO of RCKRBX, summarized it simply:
“One thing that people often forget about AI — it’s only as good as the information that feeds it. If you have garbage data, you’re still going to get garbage out.”
Tyler Christensen, Head of Industry Solutions at Cherre, explained why this is especially hard in real estate:
“It’s not that difficult to automate the movement of data… where automation gets tricky is the quality of the data… when a validation rule has tripped, you can’t automate the remediation process because it requires judgment.”
Analytics Only Matter When They Drive Decisions
Once data is structured and integrated, its value compounds.
Joe Stockton described the portfolio-level impact:
“From a portfolio view, it’s a game changer for capital planning, resource planning, looking at trends, evaluating vendors… we’re constantly raising issues for our users.”
Julie Blanc, CEO of Rentana, framed the opportunity at a broader level:
“Real estate is one of the most data-rich industries out there… applying modern AI techniques to revenue intelligence allows operators to continuously analyze market conditions and portfolio performance.”
Still, as Amber Kahr, Co-Founder of Billions, pointed out, insight must be actionable:
“I have all these reports and dashboards… but I still have to look at them every day and decide what’s important… AI should ingest all of this and tell me the outliers, the trends, and what I should do about it.”
Predictive Data Is Where the Industry Is Heading
Many leaders highlighted a shift from backward-looking reporting to forward-looking intelligence.
“99% of real estate data is backward-looking.”
Joe Stockton shared how more granular datasets unlock forecasting:
“We can say the industry standard lifespan might be 12 years, but in Arizona or Florida it’s different… we have actuarial data to say when this brand or model tends to fail and what it should cost.”
But predictive capabilities depend on foundations:
“You need that foundational infrastructure in place first in order to be AI-ready. Once the data is clean and structured, that’s when predictive capabilities become possible.”
Data, AI, and Human Judgment Go Together
Across episodes, one theme stayed consistent: data and AI augment people — they don’t replace them.
Frank Rohde, CEO of Ownify, captured this future clearly:
“Could you have a trusted advisor supported by AI and therefore ten times more efficient than today? In my mind, that’s the future.”
Zach Schofel, Principal at Eastman Residential, showed how data already informs investment decisions:
“We analyze vacancy and pre-leasing trends… population growth, zip code data… and how that affects our rent projections.”
Zach Haptonstall, CEO of Rise48 Equity, emphasized operational discipline:
“We track all of our leads… every single property, every single month… we pay tens of thousands of dollars a year for market data… every deal is evaluated through that lens.”
And Trevor Henson, CMO of Beach Front Property Management Inc., reminded listeners that legacy systems, while outdated, still carry institutional knowledge:
“That system goes back to floppy disks… it’s old, but it also gives it credibility because it knows the industry.”
The Core Takeaway
Across the Innovation Blueprint podcast, one conclusion stands out:
Real estate’s biggest data challenge isn’t analysis — it’s collection, structure, integration, and trust.
As Tyler Christensen summarized:
“A lot of companies want to jump straight to analytics or AI, but if the data foundation isn’t right, you end up spending more time explaining why numbers don’t match than actually using the insights.”
Once that foundation exists, analytics, forecasting, and AI stop being theoretical — and start delivering real value.
Join the Conversation
The Innovation Blueprint podcast, hosted by ORIL, brings together the builders and operators shaping the future of real estate technology.
If you’re working on:
- Real estate data platforms
- PropTech products
- Analytics, AI, or operational systems
…and have real lessons to share, we’d love to hear from you.
Apply to join the Innovation Blueprint podcast as a guest. Or contact ORIL to explore how we help real estate companies design scalable, data-driven software platforms.
