Commercial real estate faces a fundamental challenge: as properties become more operationally complex, the tools used to financially analyze them aren't keeping pace which in turn risks the investability of the asset class.
The industry’s complexity has grown on multiple fronts:
- Flexible space requirements and variable pricing models
- Mixed-use properties with multiple operators and operating models
- Complex service and amenity offerings
Tenants are demanding this complexity and are willing to pay a premium for it, but on the supply side, investors and lenders require predictability. They shy away from properties with "too much" complexity.
The issue ultimately boils down to an inability to model more volatile income. Commercial real estate has always been modeled along the same lines as bonds, but with increased operational requirements, real estate is now behaving more like equity. Without the tools to complete more sophisticated risk analysis, potentially profitable opportunities are being passed over, and the commercial real estate industry risks having stranded assets.
Yet even with these changes, the underlying business of real estate is not that complex. Industries like pharmaceuticals, mining, finance and insurance regularly model far more complex cash flows with sophisticated tools using methods that have been proven over decades.
The complexity in real estate comes not from the properties themselves, but from the limitations of our analytical tools.
The solution requires combining these proven modeling approaches with software that can handle the increased complexity while being straightforward to use. Combined with modern collaboration and version control tools, the software will allow real estate stakeholders the ability to both understand and communicate the true risks and returns of modern real estate operations.
The problem: when tools become barriers
The commercial real estate industry finds itself at a crossroads–market demand is pushing properties toward more complex and dynamic operating models. Yet our ability to understand and value these opportunities remains stuck in an era of traditional long-term leases.
This isn't just a matter of market preferences—it represents a fundamental disconnect between how modern properties operate and how they are analyzed. At the heart of this disconnect lies a complex web of valuation challenges, risk perception issues, and technological limitations that together create significant barriers to market evolution.
When financial models aren't understood and risk can't be measured, the prudent choice is often to avoid making the investment or loan entirely. This creates a paradox where potentially profitable opportunities are passed over not because they're too risky, but because our tools can't adequately explain their risk.
As a result, properties with "too much" complexity are seen as impaired and property owners cannot supply a more profitable product because it's perceived as too risky. In short, despite a profitable market, real estate owners are at risk of some of their assets becoming uninvestable.
Risk perception vs. reality
The issue isn't actual risk—it's the inability to quantify and communicate risk effectively. Other "risky" industries have already solved similar challenges.
The Pharmaceutical Industry models complex clinical trial outcomes with multiple revenue scenarios with longer time horizons than real estate. The Mining and Natural Resources industries need to model volatile commodity prices, complex operational variables while incorporating geological uncertainty against large capital investments. The Insurance and Reinsurance industries are required to model complex probability distributions with multiple risk scenarios while maintaining long-term financial stability.
And many areas of the financial markets such as Mortgage-Backed Securities need to model complex scenarios involving prepayments, defaults and loss with thousands of individual loans with varying terms.
All of these industries use modern financial modeling tools and are able to clearly communicate risks, providing confidence to investors and lenders, resulting in massive, liquid markets despite their cash flow complexities.
New models
More sophisticated cash flow modeling and valuation methods already exist for real estate. The Tiered Direct Capitalization and Top Slice valuation methods differentiate the traditional and flexible income streams. Monte Carlo sampling combined with the Discounted Cash Flow method can be used to model both cash flows and their levels of risk.
As other industries have demonstrated, complex and volatile cash flows can be effectively modeled and communicated to investors. But in real estate, these tools are rarely used in practice. The barrier isn't mathematical complexity; these methods are simpler than many financial models used daily in other industries. The barrier is the reliance on the spreadsheet as the primary cash flow modeling tool, which is impractical for more sophisticated modeling.
Where spreadsheets fall short
While spreadsheets are powerful tools, their designs are limited by three dimensions – the data is placed in a cell referenced by its row, column and sheet.
To overcome this restriction, spreadsheet users layer in logic and database-like functions, which increases complexity, leading to more time spent managing formulas. Complex spreadsheets are error-prone, resulting in degraded performance and wasted time.
With increased complexity, the level of transparency also drops. Simple spreadsheets can be easily followed by multiple users, but complex spreadsheets are better suited for a single-user. Complex spreadsheets cannot be easily standardized and often only one person – typically the person who built the original model – understands how they work. This makes collaboration near impossible and expensive.
With models that require multiple authors, this often results in multiple versions, creating confusion and slowing workflows. With a limited ability to track changes or maintain audit trails, spreadsheets often branch off, with team members unknowingly working on different versions.
And if an organization pushes through all of these barriers, spreadsheets are ultimately the wrong tool. They are inefficient at handling large transaction volumes which make modern cash flow modeling tools like Monte Carlo simulations impractical.
What a potential solution must deliver
If spreadsheets are to be succeeded, the replacement software solution must meet the following core requirements:
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Underlying technical ability must be powerful
The software needs to be able to run sophisticated financial modeling like Monte Carlo analysis and compare forecasted and actual results. This requires the ability to handle large volumes of data efficiently.
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Building a complex model must be straightforward
While model builders need to be able to construct cash flow models that match the operational reality. Using the software must be quicker and easier to build than using a spreadsheet.
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The results must be transparent
The software has to be thoroughly auditable. All stakeholders need to be able to easily validate cash flow modeling assumptions. The calculations can’t be a blackbox. All stakeholders should have a full understanding of how cash flow forecasts are determined.
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The onboarding experience must be seamless
Getting a user to try a new tool can be tough when they are proficient with the process they already have. The experience cannot be inconvenient, onerous or require a large upfront investment that could prevent them from trying it out.
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The software must improve collaboration
While providing a high level of openness, the software must also version model changes and provide strict access controls, improving the productivity and collaboration of teams.
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The software must be open
The software should be in an open format. All stakeholders can get access to the information they need. It should also provide a common language for discussing and understanding both performance and risk
How can this be achieved?
A solid application will need to address each of these concerns.
- Implementing a transaction-based architecture
- Having a great user interface
- Have modern collaboration tools
- Auditable and transparent
Transaction-Based Architecture: Accounting software vs. a calculator
Switching to a transaction-based architecture enables the implementation of more sophisticated modeling methods which can better reflect modern property operations. Accounting programs are an example of software based on a transaction-based architecture where at the lowest level, it is simply a collection of journal entries. These journal entries can be aggregated to produce a variety of standard and custom reports. At the user interface level, items, customers, vendors, invoices and bills group journal entries in a way that matches the reality of how the business operates.
Cash flow modeling software built on transaction-based architecture would be very similar to accounting software. Much like how accounting software can produce repeating invoices, the user would enter in ‘rules’ or assumptions which would produce a list of transactions with all the necessary data attached.
An example for real estate is a leasing rule that forecasts replacement leasing activity.
Switching to a transaction-based architecture provides several advantages:
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Transaction data can be stored in a way that allows the software to handle much larger amounts of data, opening up more powerful modeling tools like Monte Carlo simulations, scenarios and forecasts vs. actuals.
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Each transaction is an atomic unit allowing the software to ‘bucket’ the data in any way that is wanted. This provides the ability to add in audit functions as well as more reporting options.
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The results are much closer to accounting data and can therefore be easily converted to pro forma accounting transactions. It would take little additional effort to produce pro forma financial statements that matched a chart of accounts.
Simple and clear: Design and workflow from modern web applications
While the underlying architecture may be sophisticated, the user experience needs to be straightforward and intuitive. Modern web applications have mastered this balance—handling complex backend operations while maintaining simple, user-friendly interfaces that guide users and prevent errors.
Data entry: Guided and validated
The data entry workflow mirrors how real estate professionals naturally think about their properties and build their analyses.
Instead of forcing users to translate their understanding into spreadsheet formulas, the system follows the logical progression of how properties operate. From defining physical spaces and filling them with tenants, to operating expenses, to purchase and financing and finally disposition.
Like checking out from a well-designed online store, the data entry process should guide users through each step while providing immediate feedback. Rather than facing a blank spreadsheet, users are presented with clear, purpose-built forms that:
- Follow the natural thought process of analyzing a property
- Make it obvious what information is needed and where it goes
- Show immediate results as data is entered
- Validate entries as you go—catching and explaining errors early, just as an e-commerce site would flag an invalid credit card number
- Provide visual cues about what's complete and what still needs attention
Results: clear and comprehensive
Once the data is entered, the system automatically generates a complete analysis package. Unlike spreadsheets where each new type of analysis requires building new formulas and charts, the system automatically produces:
- Standardized reports that follow industry best practices
- Clear visualizations that help communicate key insights
- Risk metrics that would be impractical to calculate in spreadsheets
- Multiple views of the same data tailored for different stakeholders:
- Traditional financial metrics for lenders
- Risk analysis for investors
- Scenario comparisons for decision makers
- Historical performance tracking for operators
- Custom views that maintain consistency while highlighting relevant information
Modern collaboration tools: Software development inspired user and version controls
Modern collaboration tools solve a problem that has plagued spreadsheets since their invention—how to effectively work together on complex models.
The software development industry has already solved this challenge. When thousands of developers work together on products far more complex than cash flow models, they use sophisticated version control systems that track every change, maintain a complete history, and manage contributions from multiple team members.
Applying these proven approaches to real estate analysis provides several key capabilities:
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Version control tracks every change made to a model, creating a complete history of how analysis evolved. This means you can see who changed what and when, compare different versions, and even roll back to previous states if needed.
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Clear ownership and permission systems ensure that while many people can view and comment on models, only authorized users can make changes. Like a software project's maintainer controlling what code gets merged, model owners maintain control while enabling broad collaboration.
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Systematic audit trails automatically document who made each change and why. Instead of digging through email chains about model updates, all communication about changes is stored with the model itself.
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Knowledge preservation becomes automatic as discussions, decisions, and methodologies are captured alongside the model. When team members change, the context behind modeling decisions isn't lost.
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Change documentation follows software development best practices where every update requires a clear description of what changed and why. This creates a searchable history of model evolution that helps everyone understand how and why the analysis developed.
Auditable and transparent: A modeling, reporting and learning tool all-in-one
Modern analysis tools must balance sophistication with clarity. While complex modeling capabilities are essential, the ability to examine and verify those models is equally important. This requires an integrated approach to documentation, learning, and verification.
Transparency at every level
The analysis should be clear at every level of detail. Overview reports should exist for quick high-level understanding with reports viewable at each aggregation level. Tooltips can show off each calculation inline with the reports making every cell fully auditable.
Easily accessible documentation
Documentation should be integrated directly into the system through tooltips, companion documentation, and links allowing the user to be productive and remain in their current state of thought. Tooltips should provide high-level insights into form fields and how the field impacts the model's execution. Companion documentation should track each form on large screens and provide near comprehensive information on the active element. Application documentation should be exhaustive and provide detailed technical information for those who want to read through it separately. Samples, examples and illustrated concepts can be added to deepen understanding.
Learning integration
Similar to accounting software, a companion course should provide the underlying theory behind how real estate is modeled and how the software operates upon it. It should cover the theoretical concepts and directly tie them to application. Spreadsheet exercises can explain what is happening 'underneath the hood' and indirectly show the benefits of any software package to solve. Samples in the course should match samples in the software to work through the solution in multiple ways.
Publishing Center
The Publishing Center provides a secure repository for finalized analyses:
- Published versions remain unchanged
- Full audit trail of assumptions and calculations
- Stakeholders can examine all aspects of published models
- Creates reliable reference points for all parties
- Maintains integrity while enabling detailed review
Open system
Any viewer of the property should be able to adjust the published reports, view the assumptions and hover to view how any value was calculated. No hidden assumptions or black box calculations. Reports must be exportable to a variety of file formats to be further analyzed without vendor lock in.
Conclusion
The growing complexity of commercial real estate operations demands a new approach to analysis. The challenge isn't that properties are too complex to understand—it's that our traditional tools aren't built for today's operational reality. By adopting proven modeling approaches and combining them with modern collaboration tools, the industry can move beyond the current paradox where profitable flexible models are avoided due to perceived risk. This can create a more efficient and dynamic market that better serves both property owners and tenants.
For property owners, operators, investors, and lenders seeking to understand their real estate operations in a changing market, sophisticated modeling tools combined with open standards offer the path forward in an increasingly complex environment.
To learn more about how ReturnSuite can improve your real estate analysis, visit our website and get immediate free access to our software.