Structuring Property Data: How openOM Fits Into Your CRE Technology Stack
Short answer
Structuring property data means organizing offering-memorandum information into machine-readable formats so your CRE technology stack can share it across portfolio, accounting, and valuation tools without manual re-entry. This reduces reconciliation work, eliminates the hours spent recreating information, and lets underwriting teams apply consistent metrics across every deal.
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Why Property Data Structure Matters in a Fragmented CRE Tech Stack#
The commercial real estate industry moves capital at scale. Outstanding mortgage debt in the CRE sector at the end of 2024 totaled $6 trillion, yet the infrastructure to move property intelligence across the tools that underwrite, manage, and value that capital remains fractured. Every spreadsheet, every manual data entry, every time an analyst re-keys a rent roll into a separate system is a friction point where error enters and decision-making slows.
Roughly 83% of executives believe their companies have silos. That is not a technology problem alone; it is a decision problem. When property information lives in disconnected systems, underwriting teams cannot apply consistent criteria across a portfolio. Valuation models pull from different sources. Portfolio managers reconcile conflicting versions of the same asset. The cost is not just wasted time; it is capital deployed on incomplete or misaligned information.
The $6 Trillion Integration Problem: Why Siloed Data Costs Capital#
The stakes are concrete. 97% of executives say siloed data has had a negative effect on their business. Beyond the headline, the mechanism is clear: when data does not flow between systems, teams stop trusting the numbers, and 65% of employees stop using data for decisions altogether when needing to pull information from multiple systems, effectively halting the informed capital allocation that deals depend on.
Employees waste 5.3 hours every week waiting for data from colleagues or recreating existing information. Multiply that across a team of underwriters, asset managers, and analysts, and a single property's data journey becomes a bottleneck. The average team wastes over 20 hours per month (or 6 work weeks per year) due to poor collaboration and communication. For a CRE firm making decisions on $6 trillion in outstanding debt, that lost productivity is not a convenience issue, it is a capital allocation issue.
How openOM Centralizes Property Intelligence Across Your Tools#
Structuring property data means organizing offering-memorandum information, the rent rolls, expense schedules, tenant details, property specs, and financial projections that define a deal, into a machine-readable format that every tool in your stack can ingest without manual translation. An open standard like openOM (an MIT-licensed specification for verifiable CRE data) makes that possible by defining how property data is encoded, validated, and shared so that portfolio, accounting, and valuation systems can all read the same information without re-keying or translation.
The discipline of Commercial Real Estate Technology exists precisely to close the gap between fragmented data sources and unified decision-making. When property data is structured once and shared across your portfolio system, your accounting platform, and your valuation model, reconciliation becomes automatic. Underwriting teams apply the same metrics to every deal. Asset managers see current information without waiting for a report. The time employees spend recreating information shifts to analysis instead.
Core Property Metrics That Drive CRE Decision-Making#
Underwriting at scale requires consistent rules. Three widely used heuristics, income-to-value screening rules and a component rule, form the backbone of quick property viability screening. Understanding what each measures and how to encode it into your data structure is the foundation of repeatable underwriting.
The 3-3-3 Rule: Quick Underwriting at Scale#
The 3-3-3 rule is a rapid feasibility screen for acquisition deals. It holds that a buyer should plan for an initial capital contribution, closing costs, and reserves as three distinct components of deal structuring. Together, these three components represent the minimum liquidity a buyer should hold to close and stabilize a property without refinancing or additional capital calls.
In practice, the 3-3-3 rule is not a hard constraint but a starting point for structuring a deal. A buyer with stronger cash position or a property with lower risk may adjust the capital structure; a deal in a volatile market or with deferred maintenance may require higher reserves. The rule's value lies in its simplicity: it forces a conversation about capital structure before underwriting gets deep. When property data is structured to capture purchase price, down payment, closing costs, and reserve requirements as discrete fields, your underwriting system can flag deals that fall outside your firm's 3-3-3 parameters automatically, rather than waiting for an analyst to notice the gap.
Income-to-Value Screening Rules: Operational Viability Thresholds#
Income-to-value screening rules are used to separate viable rental properties from speculative ones. One common threshold holds that annual rental income should represent a percentage of the purchase price. Another more conservative threshold requires that annual rent should represent a percentage of the property's estimated value for the investment to generate sufficient cash flow to cover debt service, maintenance, and management without relying on appreciation.
Neither rule is universal. A property in a high-appreciation market may underperform on income-to-value screening and still be viable if the buyer is betting on capital gains. A stabilized, fully-leased multifamily asset in a supply-constrained market may exceed conventional thresholds. But as screening tools, they work: they separate deals that generate income from deals that are bets on future value. When your property data structure captures purchase price, annual rent, property value, and lease terms as machine-readable fields, your system can calculate income-to-value ratios and flag outliers without manual review.
The 5 P's of Real Estate: A Framework openOM Structures Into Your Data Model#
The 5 P's, Price, Property, Place, People, and Profit, form a complete framework for property evaluation. Each P maps to a category of data your CRE technology stack needs to capture and share.
Price is the acquisition cost, debt structure, and cap rate. Your data model should encode purchase price, loan amount, interest rate, amortization period, and the implied cap rate (net operating income divided by purchase price). Property is the physical asset: building type, age, condition, square footage, unit count, lease expiration dates, tenant credit, and deferred maintenance. Place is location: submarket, proximity to transit, demographic trends, competing supply, and zoning. People is the operator and tenant base: the sponsor's track record, tenant quality, lease terms, and turnover history. Profit is the financial outcome: net operating income, cash-on-cash return, internal rate of return, and sensitivity to rent growth or expense inflation.
A structured data model that captures all five P's lets underwriting teams compare properties on the same dimensions. It also lets your valuation model pull consistent inputs. When Place data (submarket rent trends, cap rate compression) feeds into Profit calculations automatically, the model updates when market conditions change, rather than waiting for an analyst to manually adjust assumptions.
Mapping Property Data Into Your Existing CRE Workflow#
How Standardized Property Data Reduces Manual Entry and Reconciliation#
The cost of manual data entry is not just the time spent typing. It is the error rate that comes with re-keying, the version conflicts when two systems hold different numbers, and the delay while someone tracks down which version is correct, a core friction point covered above that slows decision-making. 33% of finance teams in 2025 report struggling with inaccessible or siloed data. That struggle shows up as reconciliation work: a portfolio manager pulls a rent roll from the property management system, an analyst re-enters it into the underwriting model, and the accounting team imports it again into the general ledger. Three systems, three versions, three opportunities for the numbers to diverge.
Standardized property data eliminates that re-entry. When an offering memorandum is structured once in a machine-readable format, rent rolls, expense schedules, tenant details, all encoded in a consistent schema, every downstream system can ingest it directly. The portfolio system reads the current rent roll. The valuation model pulls the same rent roll and expense data. The accounting system imports the same figures. No re-keying. No version conflicts. No reconciliation delays.
The practical outcome: underwriting cycles accelerate. 67% of real estate executives cite data fragmentation as one of their top three operational challenges. Removing that friction is not a technology upgrade; it is a competitive advantage in a market where capital moves fast.
Connecting openOM to Your Portfolio, Accounting, and Valuation Tools#
Most CRE firms run three or four core systems: a portfolio management platform (tracking assets, performance, and strategy), an accounting system (recording transactions and generating financial statements), a valuation or underwriting tool (modeling returns and stress-testing assumptions), and often a property management system (managing leases, tenants, and operations). Each system has its own data model. None of them talk to each other without manual intervention.
An open standard like openOM bridges that gap. It defines how property data should be structured, what fields an offering memorandum must contain, how rent rolls should be formatted, what tenant and lease information is required, how to encode property condition and deferred maintenance. When your portfolio system exports property data in openOM format, your valuation tool can ingest it directly. When your property management system updates rent rolls, it can publish them in openOM format, and your accounting system can pull the same data without re-entry, enabling portfolio, accounting, and valuation systems to work from unified information without manual translation.
The integration is not automatic; it requires that each system support the standard. But once it does, the payoff is immediate: a single source of truth for property information, no manual reconciliation, and underwriting teams working from the same numbers. Sarthak Nimbalkar, Independent Software Engineer and Creator of openOM, has found that the discipline of standardizing property data forces teams to define what information actually matters and how it flows through their decision-making process. That clarity alone, before any integration happens, often surfaces gaps in how firms currently capture or share property information.
Structuring Your Own Property Data: A Practical Next Step#
The path forward is not to wait for every vendor to adopt a standard. It is to audit your own property data workflow and identify the friction points where manual entry, version conflicts, or delayed information slow your decisions.
Start here:
Map your current data flow. Trace a single property from acquisition through underwriting, portfolio management, and ongoing asset management. Note every time someone re-enters data, every system that holds a different version of the truth, and every report that takes time to assemble because information lives in different places.
Define your core property fields. What information do you absolutely need to underwrite a deal? Rent rolls, expense schedules, tenant credit, property condition, market comps, debt terms. Write it down. That is the schema your data structure should enforce.
Standardize the format. Whether you adopt openOM or build your own schema, make it explicit and machine-readable. A spreadsheet template is a start; a JSON schema or CSV standard is better. The goal is that every property record in your firm looks the same, so systems can parse it without guessing.
Test integration with one tool. Pick your portfolio system or your valuation model. Export property data in your standardized format and import it into that tool. Does it work? What fields are missing? What assumptions did you have to make? That test will tell you whether your schema is complete and whether your vendors can actually support it.
The AI market in real estate is expected to grow from roughly $223 billion in 2024 to $312 billion by 2029, and much of that growth will rest on better data infrastructure. But you do not need to wait for the market to mature. The move is to make your offering memoranda machine-readable now, to structure property data so it flows across your stack without friction, so underwriting is consistent, and so capital decisions rest on current, unified information. That is the foundation every CRE technology investment should rest on.
| Impact Category | Statistic | Business Consequence |
|---|---|---|
| Executive Perception | 97% of executives report negative effect on business | Data fragmentation undermines confidence in decision-making |
| Workforce Behavior | 65% of employees stop using data when pulling from multiple systems | Critical asset decisions made without complete information |
| Industry Prevalence | 75% of CRE industry struggling with data silos | Widespread operational inefficiency across sector |
| Finance Teams | 33% of finance teams report inaccessible or siloed data | Capital allocation delayed or misaligned with property intelligence |
| Weekly Time Loss | 5.3 hours per employee weekly waiting for data | Significant productivity drain at portfolio scale |
| Annual Productivity Cost | $1.8 trillion lost in U.S. businesses annually | Siloed data as material operating expense |
| Underwriting Rule | Primary Purpose | What Property Data Must Capture | Decision Signal |
|---|---|---|---|
| 3-3-3 Rule | Rapid capital structure feasibility screen for acquisitions | Purchase price, down payment, closing costs, reserve requirements | Flags deals falling outside firm's liquidity parameters |
| Income-to-Value Screening | Separate rental viability from speculative positioning | Annual rent, purchase price, property value, lease terms | Calculates income-to-value ratios and identifies outliers automatically |
| Debt Service Coverage | Ensure property generates sufficient cash flow | Annual net operating income, total debt service obligations | Validates property can cover debt without refinancing |
| Cash-on-Cash Return | Measure annual return on actual capital deployed | Down payment amount, annual cash flow after debt service | Evaluates whether deal meets firm's return threshold |
| Metric | Scale | Relevance to Data Structuring |
|---|---|---|
| Outstanding CRE Mortgage Debt (end 2024) | $6 trillion | Capital volume where fragmented data creates compounded decision risk |
| Properties Available for Analysis | 120 million+ | Data universe requiring standardized structure for comparative underwriting |
| Real-Time Data Signals Available | 1 billion+ | Operational intelligence requiring unified access across tech stack |
| AI Market Growth (Real Estate) | $223 billion (2024) to $312 billion (2029) | Expanding capability dependent on structured, machine-readable property data |
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Frequently Asked Questions
What is the 3-3-3 rule in real estate?
The 3-3-3 rule is an acquisition feasibility screen requiring a buyer to structure deals with three distinct capital components: initial capital contribution, closing costs, and operating reserves. Together. These represent the minimum liquidity needed to close and stabilize a property without refinancing. It is not a hard constraint but a starting point; stronger cash positions or lower-risk properties may justify adjustments, while volatile markets or deferred maintenance typically require higher reserves.
How does structuring property data improve underwriting speed?
When property data captures discrete, machine-readable fields, purchase price, down payment, closing costs, annual rent, lease terms, property value, underwriting systems can automatically calculate screening ratios (income-to-value, debt service coverage) and flag deals falling outside firm parameters without manual review. This shifts analyst time from data assembly and reconciliation to substantive analysis.
What percentage of real estate executives identify data fragmentation as a major operational challenge?
67% of real estate executives cite data fragmentation as one of their top three operational challenges, making it a material competitive and strategic issue across the sector.
Why do employees stop using data when it is siloed across multiple systems?
Siloed data forces employees to pull information from disconnected sources, creating assembly friction and trust erosion. When 65% of employees stop relying on data altogether rather than navigate multiple systems, organizations lose the decision discipline that structured, accessible property intelligence provides. The time cost becomes a barrier to using data at all.
What specific property fields must a data structure capture to automate income-to-value screening?
Property data structure must encode purchase price, annual rental income, estimated property value, and lease terms as machine-readable fields. These discrete data points allow automated calculation of income-to-value ratios and identification of properties that fall outside conventional viability thresholds without requiring manual analyst review.
How does standardized property data reduce capital allocation risk across a $6 trillion sector?
When property data is fragmented across disconnected systems, underwriting teams apply inconsistent criteria, valuation models pull from conflicting sources, and portfolio managers reconcile misaligned versions of the same asset. Structured, standardized data ensures consistent metrics across deals, aligned risk assessment, and decisions made on complete rather than incomplete information, directly reducing misallocation risk at scale.
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- Why Data Silos Are Killing Your Real Estate ROI - Hexalytics (2026-09-01)
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