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Identify which data sources to work with
Start by deciding which sources are in scope. Narrowing the surface area at the beginning is what makes the rest of the plan executable rather than aspirational.
Most apartment owners have more property data than they can use, and no reliable way to compare one property against another. Standardized data multifamily operators can trust, in one format and one environment, is what closes that gap. On the Multifamily Innovation® Show, Patrick Antrim discusses why it matters with Elizabeth Braman, Co-Founder and CEO of Revolution RE.
Today on the Multifamily Innovation® Show, Patrick Antrim discusses the importance of standardized and normalized data in the multifamily industry with Elizabeth Braman, Co-Founder and CEO of Revolution RE. Elizabeth's company provides tactical insights and reporting for peak apartment performance.
Listen to the full episode on the Multifamily Podcast Network.
The episode also includes announcements for the Multifamily Innovation® Summit and the Multifamily Innovation® Council, which provides support and networking opportunities for owners and operators of 1,500 units or more.
The two terms travel together and are often used interchangeably, but they describe different work. Standardized data means the same information is expressed the same way everywhere — one format, one definition, one environment. Normalized data means that information is organized into a consistent structure. The relationships between properties, units, and residents then hold up when you query them.
The reason both matter is stated plainly in the conversation: by having standardized data in the same environment and format, comparative analysis can be done to improve upon current practices and learn from the competitive set. Comparison is the capability. Standardized data multifamily teams can query the same way at every property is the precondition.
That is also why the order of operations matters. Analysis built on inconsistently formatted data produces confident answers to questions the data can't actually support. Our breakdown of the standardization process covers how property data moves from a source system into a form that supports that kind of comparison. And what makes a multifamily-specific data model different explains why a general-purpose approach struggles with apartment data.
The episode opens on clean data for AI applications, and that is not an accident. Predictive analytics and predictive models in property management, both of which come up later in the conversation, learn from whatever they are fed. If the inputs are recorded differently at every property, the model learns the inconsistency rather than the business. That is why standardization comes first and the interesting applications come second. It is also why the conversation returns to small clients: the benefit of consistent data is not reserved for the largest portfolios, because the comparison problem exists at any size above one property.
Patrick and Elizabeth discuss how using data for benchmarking, resident experience analysis, and risk management can help improve apartment property performance and retention. Those three are the anchors. Each depends on the standardized data multifamily teams need to compare like with like.
The work here includes analyzing year-over-year performance, team productivity, and performance metrics to determine the health of a particular multifamily property. Year-over-year comparison is only meaningful when the two years are recorded in the same format. That is exactly what breaks when staff, systems, or management change in between, and it is the premise behind portfolio benchmarking across properties.
Assessing property health also draws on online reviews, sentiment, and emails. These are unstructured inputs. Getting them into the same analytical environment as operational and financial data is what turns them from anecdotes into a signal.
Data can be used to identify opportunities or potential weaknesses in multifamily property performance for risk management purposes. The value here is early detection. A weakness that shows up in comparative data before it shows up in the income statement is a weakness you can still act on.
Assembling the standardized data multifamily owners need is the job of multifamily ETL and data standardization, which converts data from the various systems an operator runs into a single consistent structure.
The episode also examines the benefits of offering apartment amenities and experiences to residents, which can enhance their overall experience and increase retention. The harder question is how to measure whether any given offering is working.
One way to measure their impact is by analyzing online reviews, which are the primary means by which most people find housing these days, especially within the multifamily industry. That makes reviews both an outcome and an input. Positive reviews can lead to increased traffic from online leads, while negative reviews can have the opposite effect.
Determining the value of these offerings is genuinely difficult, but it is crucial to consider their overall impact on resident experience and retention. This is a good illustration of why standardization matters beyond financial reporting. Review content, sentiment, and resident communication only become comparable across a portfolio when they are brought into the same structure as the standardized data multifamily reporting already runs on. Comparability is what separates an amenity decision made on evidence from one made on instinct.
Elizabeth stresses the importance of having a structured and focused data strategy for multifamily companies to effectively use data and achieve results. The emphasis on structured and focused is deliberate. The common failure mode is an effort that is broad, unowned, and therefore never finished. Structure is what turns raw exports into standardized data multifamily companies can act on.
Start by deciding which sources are in scope. Narrowing the surface area at the beginning is what makes the rest of the plan executable rather than aspirational.
Goals have to satisfy all three conditions. Achievable keeps the effort alive, measurable makes progress visible, and worthwhile ensures the result justifies the work.
A data strategy that lives with one team produces data that only that team trusts. Execution has to include the people who enter the data as well as the people who analyze it.
Training turns a standard into a habit. Exploration is how teams find the use cases nobody wrote into the original plan.
Normalized data means property information has been organized into a consistent structure, so that properties, units, and residents relate to each other the same way no matter which system the data came from. It works alongside standardization, which governs the format and definition of each field. Together they put data in the same environment and format, which is what enables comparative analysis.
Standardized data multifamily owners can compare across properties supports benchmarking, resident experience analysis, and risk management, all of which improve apartment property performance and retention. It makes it possible to analyze year-over-year performance, team productivity, performance metrics, online reviews, sentiment, and emails to determine the health of a property. It also underpins revenue growth work, testing new technologies, and regulatory compliance.
Standardization is about consistency of format and definition — the same field expressed the same way across every source. Normalization is about structure — organizing that information so the relationships between properties, units, and residents are consistent and queryable. Comparative analysis requires both, because having data in the same environment is not useful if it is still in different formats.
Benchmarking, resident experience analysis, and risk management all rest on the same foundation: portfolio data standardized into one format and one environment.
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