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How Crowdsourcing is Helping Apartment Operators

How Crowdsourcing is Helping Apartment Operators

When people picture crowdsourcing in action, the apartment industry doesn’t typically come to mind.

We’re more inclined to think of big consumer brands getting ideas from their customers and the general public. Like the popular LEGO Ideas platform, through which users can submit their suggestions for new LEGO sets.  Or Waze, which has allowed many of us to avoid traffic and find the best routes by sharing information instantaneously with one another. Another great crowdsourcing platform we use without thinking about it much is Wikipedia, which far outstripped Encyclopedia Britannica with wealth of information and data. These are but a few examples of platforms that used crowdsourcing to offer better, faster, more relevant data and information that we rely on daily basis.

But the truth is, more and more apartment communities are embracing crowdsourcing in a very effective way: They’re using it to make their market surveys more efficient and more accurate than ever before.

Old School Isn’t Cutting It
Undoubtedly, you are familiar with the old-school, traditional way apartment communities have gone about conducting market surveys of their competitive set. It’s unwieldy, time-consuming and, too often, results in inaccurate data.

Onsite associates who are too busy to begin with have to spend hours making phone calls to competitive properties to collect data about asking rents, occupancy rates and concessions. Frequently, the person at the other property they need to talk to isn’t immediately available, necessitating rounds of phone tag. Many times, the overextended person at the other community never calls back.

Adding to the difficulties is the fact that associates often aren’t trained to ask the kinds of questions that will ensure the data they receive is accurate and represents a true apples-to-apples comparison between properties.

These days, however, apartment communities are learning there’s a better way.

Instead of putting such a heavy burden on their associates, they’re using online platforms to automatically exchange performance data with their comps. In other words, they’re crowdsourcing their market surveys, saving themselves and everyone around them a lot of time and effort in the process, they are giving and receiving far more reliable data.

These online exchange platforms greatly simplify, automate, and streamline the compilation of market surveys, and they standardize data entry to ensure apples-to-apples data is collected.

The Benefits of Crowdsourcing
To the outside world, it might seem strange that apartment communities would turn to a form of crowdsourcing to so easily exchange performance data.

But multifamily operators understand the vital importance of knowing exactly where their communities stand in their competitive sets. And, in the end, the only way to gain that understanding is to swap accurate information about rents, occupancy rates, concessions, etc. Think of it as, “You scratch my back, I’ll scratch your back,” but with a wider network of properties in your submarket that automatically swap data without the manual effort required to collect it. It also brings transparency and accuracy when more than one property can participate simultaneously.

Exchanging this kind of information isn’t giving away state secrets or paving the way for your community’s doom. It’s a way to get the information you need to make the right pricing and operational decisions.

Besides, just because another community knows your rental rates doesn’t mean it will then be able to attract and retain residents simply by going out and pricing its units lower than yours. Signing new residents and keeping existing ones depends on a wide range of factors, and a community’s ability to do these things isn’t going to be negatively affected because of the free exchange of comp data.

When all is said and done, smart operators realize that they can only strengthen their communities by using a crowdsourcing platform to exchange comparative property data.

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