Skip to content

The Multifamily Guide to
Business Intelligence

What is Business Intelligence?

Business intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.

BI consists of four key functions which build in complexity and business value: reporting, analysis, monitoring, and prediction.

  • Reporting is where a lot of people start and end their relationship with data. Reports are static and don’t allow users to dig deeper or look for trends, thus limiting the insights they deliver. Reports, queries, and search tools tell us what has already happened and where we are today. No more, no less. While important for many business purposes, they typically offer limited value for forward-thinking decisions. 

  • Analysis allows us to focus on why things happened. Understanding the why helps contribute to making good decisions moving forward. Visualizations such as graphs and infographics can connect data elements and present them in a way that makes their relationships more obvious; statistical processes can give us a sense of how reliable those conclusions are; and Online Analytical Processing (OLAP) tools help drill into the data, exploring relationships that weren’t immediately obvious without granular or aggregated analysis.

  • Monitoring offers us a real-time view of our business from a data perspective. With the gift of here and now, we can course-correct future results, catch an issue or simply be more empowered knowing the state of your business. Dashboards, scorecards, and alerts give us the tools to proactively drive great results instead of reporting on the bad after the fact. This is also known as “operational business intelligence,” or “real-time business intelligence.”

  • Predictive analytics are the “holy grail of BI”, processing data to provide insights to help us predict the future. When executed well, predictive analytics offer us the gift of time. Time to preemptively impact the future before it is here.

Download for Later: The Multifamily Guide to Business Intelligence

Download our comprehensive guide to multifamily business intelligence and start your journey to making better decisions, faster, today.

Why is Business Intelligence Important in Multifamily?

As an industry, multifamily housing has often lagged similar verticals (e.g. hospitality) in both technology and operational sophistication. That has slowly changed over the past two decades with the introduction of instantaneous credit checks, automated pricing and revenue management, web-based property management systems, and digital marketing strategies.

The demand for good multifamily business intelligence (BI) solutions is growing exponentially today. For an industry that still has associates who remember tracking physical guest cards in those green metal boxes, technology has rocketed us into an environment where we often drown in data yet struggle to get actionable insights.

This “data-rich, actionable information poor” paradigm is driven by an explosion in the number of real estate technology (RETech) solutions, each with its own set of data, some of which integrates well with other systems and some which stays quite siloed. Add to that the fee management model (representing more than two-thirds of professionally managed rental housing) where an owner’s portfolio technology stack is often spread amongst multiple different property management systems (PMSs), CRMs, and other technology tools, and you have a very complex and cumbersome data environment.

The next logical step is in the area of business intelligence (BI). Most multifamily operators want to be more data-driven in their decision process and less “gut feel” in execution but few really know how to make this fundamental transition in capability and culture. Unlike the initiatives mentioned earlier, it’s also much harder to assess and measure the ROI of BI investments. How can you identify the bad decision avoided or the good decision made that wouldn’t have been made without the BI system?

Good Rental Housing BI Graphic CTA (250 × 500 px) (1)

A Typical Business Intelligence Journey

Almost every company that ends up with a robust BI platform goes through a very predictable 3-stage evolution:
  1. Silo’d data
  2. Overlapping data
  3. Coherent BI

In stage 1, data is almost completely silo’d. Applications are mostly standalone with their own data. Departments often have their own sets of applications with their own data. Often individual associates (particularly analysts) have their own unique files they use (e.g. maintaining their own mapping table of reporting hierarchy). Data exchange, if it occurs at all, tends to be done by email attachments of Excel files and/or “sneaker LAN” transfers via thumb drives or similar. Visualization is typically limited to Excel graphs.

 

As the limitation of these data silos impedes an organization’s ability to leverage its own data, companies naturally evolve into Stage 2. Analysts begin to build increasingly complex spreadsheets, often drawing from multiple data sources. At first, these seem like great progress, but soon a vicious cycle develops. The increased capability of these spreadsheets drives a desire for more connected

information which then leads to more complexity. Soon, these “spreadmarts” are so intertwined and complex, that even their original authors are never 100% sure exactly how they were built and how to use them.

A sort of parallel evolution further characterizes this stage. Multiple departments create their own spreadmarts with occasional, but not very well coordinated overlaps. The natural and logical consequence of this is a proliferation of metrics and reports that never quite match each other. Even something as simple as “occupancy” ends up slightly different depending on which report you look at thus substantially eroding executive and field confidence in any of the numbers. Improved automation helps, but analysts still end up spending more time collecting and collating data than analyzing it. As they continually chase various data anomalies, they often feel like they’re stuck in a corporate version of the “whack a mole” arcade game.


When the complexities, conflicts, and frustrations of this stage become large enough, companies are finally motivated to invest enough to “cross the BI chasm” and arrive at the final stage of coherent BI. This is characterized by a strategic approach to understanding key business drivers, developing appropriate (enterprise) data models, and deploying applications in a well-conceived BI portfolio. This “single source of truth” drives confidence as all users build off common definitions for metrics and a common data platform.

Evaluating a Good Business Intelligence Platform

A good BI platform meets three key criteria:

  1. A strong business focus implemented by contemporary data modeling principles
  2. Provides users what they want, how they want it
  3. Meets modern “table stakes”

Data modeling

It may sound trite, but a good BI platform allows users to see what is important to them for making business decisions while not cluttering things up with data that isn’t important at that moment and in that context. And the reality is that it takes a lot of purpose-driven effort to make this seemingly obvious statement come to life. The first step to achieving this is to define the most important business factors worth paying attention to.

Along with establishing the CSFs and KPIs/KRIs, a good BI platform models data based on a specific methodology. This ensures that the relationships between the data are well understood, that the data in the data warehouse will be able to answer the key business questions posed by the CSFs, and reduce the overall development (and more importantly) ongoing maintenance costs.

While no methodology is perfect, the Kimball methodology has several advantages that are particularly appropriate for multifamily. This methodology defines a specific set of processes and techniques to design, develop and implement an enterprise data warehouse. Kimball is a bottom-up approach to creating a data model and then managing the data warehouse life cycle.

What I want, how I want it

The notion that any one dashboard or report would be great for a wide range of users is a bit of a fool’s errand. After more than 30 years of analytics work, we know that different clients and different users within each client have different desires; and even for the same data, some people love charts, others love tables, etc.

That’s why a good BI platform has tremendous flexibility in its user interface. Different clients and users can configure dashboards however they want. To do this, the platform must allow for database extensibility—the ability for users to add data elements to a core data warehouse. This requires some strong technical skills, so not all users will want to do this. But for those who have the skill, it means there’s no ceiling on what can or cannot be in the warehouse; and for those who don’t, it’s still good to know they can do so in the future if they want to hire the appropriate resources.

A good BI platform also recognizes there are two distinct types of users. Most users will consume published dashboards. However, power-user analysts want complete ad hoc analysis capabilities. Think about the pricing and revenue management (PRM) associate, the financial planning and analysis (FP&A) team, and maybe some highly analytical asset managers. These are the “Excel Jocks” who typically do all the data collation and reporting when there is no BI platform (or the platform doesn’t really meet the company’s needs).

In the “spreadmart” world of 80% data collation and 20% data analysis, this is a painful request. But with a good BI platform, this analysis can be re-run at the press of a button—99% analysis and 1% administration. And inevitably, executives will see some of these repeated analyses and realize they should share this information with others. Thus, is born the inspiration for a new published dashboard or report!

Table stakes

While all the attention (and differentiation) may be on the data modeling and visualization layers, there are several “tables stakes” that a good BI platform must meet:

  • Access must be available 242x7x52 anywhere there’s a Wi-Fi or cell data connection
  • Visualizations and reports should render well on desktop or laptop devices. Given the density of data in typical dashboards, it’s rare that they render well on mobile phone screens
  • Modern security, e.g., disk-level data encryption, encryption for data transfers, periodic vulnerability testing, etc. is in place

Limited Multifamily Business Intelligence Options

In the decades we’ve been working in multifamily technology, one of the consistent themes is the tug-of-war between “best of breed” and “bundled” approaches. The former generally provide better solutions but at the cost of integrations and complexity; the latter is certainly simpler but rarely leads to a truly top-notch tech stack.

There will never be an objective “best” choice as different companies with different objectives will rightly make different choices. However, we’re seeing indications that the bias has moved back towards best of breed approaches.

Until now, operators have faced limited multifamily business intelligence choices:

  • Build reports by collating data from various SOR reports or extracts
  • Build a bespoke BI platform
  • Use an “off the shelf” solution, typically offered by their PMS
REBA Webinar Replay CTA

Building reports from collated data

Based on our experience, this is the choice that most operators have made. The good news is that users can create and get reports; the bad news is that it’s highly manual, often constraining and almost always frustrating. As spreadmarts proliferate, they become more mission critical…and they also become more prone to errors or differences across the various data silos.

Signs that you are feeling pain from this approach include:

  • Multiple reports have different numbers for the same metric (e.g. occupancy, availability, …)
  • It feels like you have an “army of analysts” to do basic reporting, both internally and to other key stakeholders
  • It’s hard (or impossible) to get data to support some key decisions

Building a bespoke data platform

While overall, most operators choose the first option, this approach is the choice of the majority of large (>30,000 units) operators. These operators have the resources to invest in such a large (and expensive) undertaking and often choose this path based on the lack of other credible alternatives for them.

There are many advantages to this approach, particularly in the areas of control and flexibility. However, there are several challenging obstacles to anyone just beginning to embark on this approach:

  • Projects of this type typically take 2-3 years to come to fruition
  • Large bespoke BI platforms easily cost $2-3M (or more)
  • Companies take on material technical debt
  • These projects are rife with total cost of ownership (TCO) “traps”

 Buy off the shelf (OTS) solutions

Historically, most of the OTS solutions have been offered by the various PMS vendors. While providing capability more robust and stable than the spreadmart world of data collation, these solutions have material limitations:

  • These platforms are not the true “single source of truth” (SSOT) that business users need.
  • These solutions typically offer limited flexibility in building dashboards.
  • These environments typically lack super user access for ad hoc queries.
  • Some of these systems pre-aggregate some data which then limits the ability to “slice and dice” the data differently as needed change in the future
  • Lastly, these solutions offer limited or no data extensibility.

Given all these limitations, we often see people using these solutions as a sort of “meta-tech.” Instead of being their SSOT, they download multiple reports from this kind of BI application into Excel and create dashboards there. This “meta tech” provides a valuable service in bringing some data together but ultimately leave users still collating data and creating spreadmarts fed by this data.


Tips for Getting Started with Business Intelligence

Here are three tips for how to get started with business intelligence:

  1.  Companies need to hire for analytic success. If you think metaphorically of business associates as “blue” people and IT folks as “red” people, then what is clearly needed is more “purple” people—i.e. people who can span the boundary between the These people are out there, but they’re not always easy to find. Their current companies value them, so they aren’t often looking for new jobs.
    • At a minimum, bring in facilitators/consultants to fill the short-term need for “purple” people. People with experience in the Parmenter and Kimball methodologies can help the business learn how to be more articulate about their needs while simultaneously understanding and reducing the risks in execution for the IT They can sometimes force the provocative and constructive conversations that are hard to initiate without a third-party facilitator.
    • Long term, search within your company for people who can span the boundary and make a conscious effort to recruit people with talents and skills in both areas
  2. Use the well-established approaches (e.g. Parmenter, Kimball, etc.) to help the business state their needs and learn more about how to “speak IT.” Implement an Agile-style approach that emphasizes short, small, and accretive iterative steps to get to the final state rather than a long and large “requirements determined upfront” swing for the fences.

  3. Once the business needs are determined, then trust IT to make the technology decisions. With the right goal now in mind, they can analyze the vast array of BI offerings out there and use their assessment and negotiation skills to choose the best suite of products for the best terms.

Technology has finally advanced to a point where multifamily owners, managers, and operators shouldn't be prevented from getting the data they need when they need it and, in the format, and modality, they need it. Nor should they have to invest in a multi-year, multi-million-dollar bespoke tech project rife with risk and technical debt.

Look for a system built by multifamily people for multifamily people and evaluate your options against the key criteria to look for when evaluating a multifamily business intelligence platform. Your teams will make better decisions, much more quickly and with less analytical overhead. Quite the win-win-win!

MF Guide Ebook Download-1