01
Alliances
Alliances and partnerships within the industry to create more integrations and channels for real estate owners and operators to use their own data. Operators should push their vendors to work together for their benefit.
Apartment owners and managers collect a lot of data from their residents, but collecting it and putting it to good use are two very different things. To fix rental property data you have to accept that centralizing it is only half the job. The half nobody budgets for is making it consistent enough to be usable — and that is the half this final part of the series is about.
The rental housing market is big, very big. Over 100 million Americans rent their housing, and it is their largest household expense. The data generated in the course of serving them is correspondingly enormous. It continues to be a challenge for owners and managers to unlock the power of that data to deliver a better housing experience and make sound business decisions.
Today many operators agree that they need a comprehensive data strategy in which all the data they collect from various sources is stored in one place. That consensus is real and it is progress. It also raises the obvious follow-up: if the data is now in one place, why is it still so difficult to use?
Collecting and storing data in one place is part of the solution, but that doesn’t mean the data is usable. You can’t create accurate reports and data models with inaccurate data. That means data must be in the same standardized format before any of it can be trusted. The bottom line is that erroneous data is useless — and a warehouse full of erroneous data is simply useless data that is easier to query. You cannot fix rental property data by moving it somewhere new.
Sure, getting everyone on board with one system would produce one standard set of data. Although that would improve the current situation, it’s improbable and exceedingly anti-competitive. The way to fix rental property data cannot depend on an industry agreeing to converge on a single vendor.
With today’s technology we can build solutions that take all these different data formats and convert them into one standard format. That’s right, folks — we now have standardization.
Consider a common scenario. Three colleagues are assigned to work on an Excel-based project simultaneously, working with data for a list of properties. The person assigning the project doesn’t give the team specific instructions on the exact format for each input field. As a result, each person enters data points based on their own experience and preferences. The state of New York could be entered as New York, NY, or even New York City. Now you have three different formats for one data field, and someone has to take the time to select the preferred format and convert the other two.
That’s a very simple example. The reality is much more complicated. Operators use, on average, 30 different stand-alone systems. So it’s not just cleaning up one row in Excel. It’s taking Excel, PowerPoint, Word, and many more programs and trying to merge them magically into one. That data merging is challenging under any circumstances, and especially so in real estate, where the data is inherently complicated.
The important thing about the New York example is that scale doesn’t change the nature of the problem, only the cost of solving it by hand. Three formats in one column is an afternoon. The same divergence across dozens of systems, hundreds of fields, and years of history is not work a person can do at all. That is why the effort to fix rental property data has to be automated to be real.
It’s a fair question. Business intelligence products have been mature for a long time, and yet operators still can’t get a clean portfolio view out of them. The reason is that real estate data doesn’t behave like the data those tools were designed around.
Real estate data is multi-dimensional, which means there’s a lot more to it than the number of cogs or widgets produced on a given day. Real estate operators want to look at a unique property over a period of time and drill down into the details — bedroom count, unit type, activities and status at the property, and the marketing and maintenance inputs. On top of that, each system records things slightly differently, without great transparency, which makes it all a bit challenging.
This is why a reporting layer has to sit on top of a standardization layer rather than directly on source systems. It is also how a multifamily-specific data stack differs from a conventional BI stack. The reporting layer is not where you fix rental property data.
A standardization layer sits between the systems that produce property data and the reports people read. It accepts each source in whatever shape that source emits, and it outputs one consistent structure. Everything downstream — reporting, benchmarking, modeling — then works from a single definition of every field rather than negotiating between several.
That is the practical answer to the constraint identified above. Since convincing the whole industry to adopt one system is both improbable and anti-competitive, the workable alternative is converting many formats into one. Our multifamily ETL and standardization product does exactly that conversion. The step-by-step standardization process shows what happens to a raw property export between the source system and a usable record.
None of this requires abandoning the systems your teams already use. That’s the point. The layer exists precisely so the diversity of systems underneath can stay where it is.
The real estate industry has some work to do, but advancements in data use are possible. The path to fix rental property data across the industry is as easy as ABC.
Alliances and partnerships within the industry to create more integrations and channels for real estate owners and operators to use their own data. Operators should push their vendors to work together for their benefit.
Belief that the data is a valuable asset to be used to provide better service to clients, yield to investors, and a superior housing experience. As we like to say, do good with good data.
Commitment to an industrywide open and transparent data strategy.
As prolific French writer Voltaire warned, we should avoid allowing “the best [to be] the enemy of the good.”
In other words, we shouldn’t get caught up in perfection to the point that we achieve nothing. The value of big data and machine learning in real estate follows this advice and understands that perfection cannot be achieved on day one. A data strategy will take some time, effort, and more than a few iterations to bear fruit. When it does, the alpha sought by the best of operators can and will be achieved.
This is the final part of a three-part series. Part One explains how the industry got to bad data through fragmentation, manual entry, and the absence of a common data structure. Part Two makes the case for why a data strategy is worth the investment.
Storing everything in one place is part of the solution, but it doesn’t make the data usable, because accurate reports and data models can’t be built from inaccurate data. To fix rental property data, it also has to be in the same standardized format. Today’s technology allows solutions that take many different data formats and convert them into one standard format, which is the practical alternative to getting an entire industry onto a single system.
Real estate data is multi-dimensional — operators need to look at a unique property over time and drill into bedroom count, unit type, activities and status, and marketing and maintenance inputs. Each system also records things slightly differently and without much transparency. A visualization layer inherits those inconsistencies rather than resolving them.
It takes data arriving in many different formats from many different stand-alone systems and converts it into one standard format. That means downstream reports, benchmarks, and models all work from a single definition of each field instead of reconciling competing ones. It works without requiring operators to replace the systems their teams already use.
Erroneous data is useless data, and the way out is a layer that converts every system’s format into one standard one before anybody builds a report on top of it.
See plans and pricing