Podcast · Multifamily Collective

Business intelligence multifamily real estate teams can trust: a conversation with Mike Brewer

Originally published June 10, 2024
LISTEN

About this episode

Business intelligence multifamily real estate operators can rely on starts long before the dashboard. It starts with getting data out of property management systems, into one consistent structure, and in front of the people making decisions. That is the thread running through this conversation between Elizabeth Braman, CEO and co-founder of Revolution RE, and Mike Brewer, recorded for the Multifamily Collective.

Listen on the Revolution RE YouTube channel.

KEY TAKEAWAYS

What the episode covers

01

BI has three layers, and the middle one does the work

Business intelligence turns raw operational and financial data into information a team can act on. In multifamily that data lives in property management systems, accounting systems, and spreadsheets. Every BI effort has three layers: the sources where data is created, the pipeline that moves and reshapes it, and the reporting layer where people read it. The pipeline is where one property’s numbers become comparable with another’s. Our step-by-step explanation of the ETL process shows how that happens.

02

Tableau and Power BI present. They don’t prepare.

Both are reporting-layer tools. They’re very good at visualizing data that is already clean, structured, and connected. They don’t extract data from a property management system, reconcile definitions across properties, or keep a portfolio-wide model current. If you have in-house analysts and a data warehouse, a general tool over a well-built ETL layer works. If you run several property management systems and have no data team, a BI tool built for multifamily portfolios is the shorter path, because it bundles the pipeline and the reporting layer so the numbers are reconciled before anyone looks at them.

03

Multifamily data was never designed to be compared

Each property management system has its own field names, statuses, and calculations. Each operator configures those systems differently. Even within one company, two properties can define occupancy two ways. A general-purpose data model flattens those differences and loses information, which is why a multifamily-specific data model is different from a generic one. The fix is a data strategy: which sources are in scope, which definitions win when systems disagree, and who owns the result.

04

Predictive analytics and AI inherit whatever sits beneath them

Descriptive reporting answers what happened. Predictive analytics estimates what is likely to happen next, from patterns in historical data. AI extends both by finding patterns people wouldn’t think to look for and by making the results easier to ask for in plain language. Every stage inherits the quality of the stage before it. A model trained on inconsistent data predicts the inconsistency.

Most disappointment with BI comes from investing in the reporting layer before the pipeline works.
WHY IT MATTERS

Standardized data turns debates into decisions

When data is inconsistent, meetings turn into arguments about whose number is right. When the same field carries the same meaning everywhere, you can compare properties, managers, and periods without first asking whether the comparison is fair. That shows up in three places:

  • Property management: spotting the property whose delinquency or turnover is drifting before the quarter closes, and comparing team performance on the same definitions.
  • Asset acquisition: underwriting a target against your own standardized operating history rather than a broker’s assumptions.
  • Portfolio strategy: answering questions across every property at once, because the data already sits in one model.

For a companion conversation on why standardized and normalized data underpin all of this, listen to the Multifamily Innovation Show episode.

FAQ

Frequently asked questions

01
Where can I listen to this episode?

On the Revolution RE YouTube channel. The conversation was recorded with Mike Brewer for the Multifamily Collective.

02
What does ETL mean for property management?

ETL stands for Extract, Transform, Load. Extract pulls data out of each source system. Transform converts it into one consistent set of definitions and structures. Load places the result where reporting tools can reach it. For a property management company, transform is where the real work happens. It is where one system’s “vacant-leased” and another’s “pre-leased” become the same status, and where every property’s income statement lands on the same chart of accounts. Skip it, and every report becomes a manual reconciliation project.

03
What is the difference between clean data and standardized data?

Clean data means fewer errors and gaps. Standardized data means the same field carries the same meaning everywhere. You need both before a comparison across properties or periods can be trusted.

Put a working pipeline under your dashboards

Standardized operational and financial data across every property management system is what makes business intelligence, predictive analytics, and AI worth the investment.

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