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How we got here
Part One traces the origins of messy rental property data through the structural causes that produced it, from a fragmented ownership market to the absence of a common data structure.
You already pay for Costar and other market reports, and your team already pours enormous energy into Excel to model, underwrite, and manage properties. So before building a rental property data strategy, the fair question is whether one is even necessary — and whether using your own data returns more than it costs. The short answer is yes, for five specific reasons, and only when the underlying data is good.
Before determining how to develop a rental property data strategy, it’s worth considering whether one is necessary at all. Most real estate companies already leverage expensive tools such as Costar and other market reports. Many certainly put a lot of energy into Excel to model, underwrite, and manage properties and portfolios. Given that, is it necessary to use your own data to develop further insights?
Real estate owners are all about the bottom line, so it’s no surprise that some balk at spending additional time on data mining and analysis. They’re not wrong. Spending time and effort to utilize data doesn’t make sense in some scenarios if there’s no sufficient return on investment. That is a legitimate position, not a failure of imagination. Any argument for a data strategy has to clear that bar rather than talk around it.
The distinction worth drawing is between the two kinds of data in play. Purchased market data tells you what the market is doing. Your own operating data tells you what you are doing, and whether that is better or worse than it should be. The second question is the one that changes decisions. No vendor can answer it for you, because no vendor has your operating history. A data strategy is simply the plan for answering it yourself.
So how do you know if it’s worth it, and what value can you actually glean from your data? A rental property data strategy delivers five things, specifically.
Having all historical financials in one place lets you use property data to inform current performance and to see how similar properties should be performing. That supports identifying and tracking seasonal trends, understanding the desirability of a specific unit type at a particular property, and isolating other key drivers. It enables concrete decisions — setting a concession strategy, or underwriting a new acquisition in a market using trusted operating history rather than a seller’s financials. The difference between those two inputs is the difference between verifying a deal and taking one on faith.
Reviewing portfolio data in one centralized location lets owners and managers develop expense benchmarking, track portfolio performance across different markets, and set accurate budgets and underwriting pro formas. Benchmarking only works when the properties being compared are described the same way. That is why centralization and standardization have to happen together rather than in sequence.
Most managers and owners are required to provide some level of reporting to customers and investors. Pulling those reports from different systems in different formats invites human error and inconsistency. The consequence is that inaccurate reports get delivered to key stakeholders. A rental property data strategy allows the generation of accurate, consistent reports for maximum accountability and visibility. This is usually the value that lands first, because the cost of the status quo is already being paid every reporting cycle.
With your own history assembled and consistent, you can find patterns in your data to identify risks and opportunities. Prediction is the last capability to arrive and the first to be oversold. It depends entirely on the quality of everything underneath it.
By its very nature, real estate is an illiquid, privately held investment with little to no public reporting requirements. But the same requirements that create transparency in the public markets are the forces that accelerated the use of big data in many other industries. Data asymmetry has been the hallmark of the best investors in real estate: we know what everyone else doesn’t know, and that’s why we are the best. A rental property data strategy is how an operator gets on the right side of that asymmetry using assets it already owns.
Take the ROI objection seriously and it becomes a useful filter rather than a blocker. The question is not whether data is valuable in the abstract. It’s whether a specific piece of work returns more than the effort it consumes, and a few practical tests separate the two.
There are compelling reasons for real estate owners and operators to devote time and resources to proper data collection, mining, and analysis. It can be a valuable tool for informed decision-making built on a fact-based foundation of good data. The emphasis belongs on good. For more on why a multifamily-specific foundation behaves differently from a general-purpose one, see what makes our approach to property data different.
The five sources of value above arrive in a sensible order, and that order is the plan. Start with reporting, because the cost of producing it by hand is already on the books and the error rate is already visible to investors. Assemble the operating history next, since every other capability draws on it. Standardize as you assemble, not afterward, so the history is comparable from the first month. Benchmarking follows naturally once the properties are described the same way. Prediction comes last, after the foundation has proven itself. An operator who runs the sequence in that order gets a return at each step instead of waiting for a distant payoff.
This is no longer a fringe position. As shown in a 2021 report from the Urban Land Institute (ULI), 70% of members surveyed indicated that data analysis will play a significant role in their strategic goals in the foreseeable future.
That matters for two reasons. The first is competitive: if a large share of the institutional field is building this capability, the advantage of data asymmetry moves from those who have data to those who can actually use theirs. The second is practical. As expectations rise, the reporting standard that investors and partners consider normal rises with them. Organizations still assembling numbers by hand each quarter feel that gap before they can close it.
Up next, let’s solve this bad data situation.
Part One traces the origins of messy rental property data through the structural causes that produced it, from a fragmented ownership market to the absence of a common data structure.
Part Three covers the fix, including why standardization rather than universal system adoption is the realistic path forward.
Expensive tools such as Costar and other market reports tell you what the market is doing, but they can’t tell you how your own properties are performing against their own history. Your operating data is what supports trusted operating history, expense benchmarking, and consistent investor reporting. The two are complementary rather than substitutes.
A rental property data strategy delivers trusted operating history for underwriting and concession decisions, benchmarking across properties and markets, accurate and consistent reporting to customers and investors, predictive analytics for identifying risks and opportunities, and transparency in an asset class that has very little of it by default. Each of those depends on historical data being centralized and consistently formatted first. Without that foundation, the analysis layer simply distributes inaccuracy faster.
Market data describes conditions outside your portfolio: rents, supply, and demand across a market. Operating data describes what happened inside it, property by property and month by month. Only the second one can tell you whether a property is performing better or worse than it should, which is why it is the foundation of any data strategy rather than something purchased reports can replace.
Trusted operating history, real benchmarking, and reporting that holds up in front of investors all rest on the same thing: property data assembled in one place and standardized to one format.
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