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Demystifying Machine Learning and AI in Multifamily Marketing Analytics

Demystifying Machine Learning and AI in Multifamily Marketing Analytics
Overview Machine Learning and Artificial Intelligence (AI) are oft-cited buzzwords in our contemporary conversations about property operations and analytics -- but what are their meanings and, more importantly, how can their application benefit multifamily business operations?  If you are encountering these concepts in internal meetings, vendor presentations, and sessions at conferences -- but wish to take a deeper dive, you've come to the right place.  This article aims to demystify the concepts of Machine Learning and discuss their role in the broader context of Marketing Analytics.  So, first, the terminology..... What is Machine Learning?  How is it different from Artificial Intelligence?  Machine learning is a method of data analysis used in computer software to identify patterns in data and make recommendations for optimization based on those patterns.  Artificial Intelligence is a broader discipline, that includes Machine Learning -- but also represents a higher level of cognition. An example of Artificial Intelligence technology would be in certain types of robotics, where a more human-like sense of reasoning would be applied -- such as with self driving cars -- whereas Machine Learning is more focused on analysis and logic.  In this article, we will focus on Machine Learning, as a fundamental concept for building Marketing Analytics systems.  How does Machine Learning work?  The basic principle of Machine Learning is that it takes a set of assigned data; finds patterns within that data; builds a model around those patterns; then tests and refines that model to make recommendations based on the patterns in that data.  What are......
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Strong Market Surveys Can Optimize Your Comp Shopping Efforts

Strong Market Surveys Can Optimize Your Comp Shopping Efforts
There are numerous benefits to optimizing and streamlining your market survey process.To start with, when you have reliable real-time data about your competitors’ rental and occupancy rates, you understand exactly how your community compares to its comp set and you can make truly informed pricing decisions.Also, when your busy onsite teams use an efficient, automated survey-compilation process, this frees them up to handle the many responsibilities they have that are critical to signing new residents and retaining existing ones.Another benefit of a market survey system that reliably collects accurate real-time data from your comps is that it eliminates the need for any type of ‘secret shopping’ you would ordinarily undertake at your competitors. Instead of worrying about collecting information on pricing and concessions and amenities, your shoppers can instead focus on the experience that onsite associates provide prospects.And that’s vital information to have.The Ripple Effect of Bad Market SurveysApartment operators are well versed in the drawbacks of manually compiling market surveys. Onsite associates who already have many other demands on their time place phone call after phone call to comparable properties to gather information about asking rents, occupancy rates, concessions and amenities. Often, the person they need to talk to isn't immediately available, necessitating rounds of phone tag; frequently, the person never calls back.Another problem stems from 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.Additionally, even if the associates are able ......
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Is Web Scraping the Answer?

Is Web Scraping the Answer?
Recently I have heard many people talking about the wonders of scraping websites and how it will solve all of our data gathering problems. And unfortunately, I must disagree. But before delving into why I believe web scraping is a flawed practice, it is important to truly understand what it is. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented by websites, data aggregators, and others using a bot or web crawler. Essentially, it is a form of copying, in which, specific data is gathered and copied from the web - typically into a central local database or spreadsheet - for later retrieval or analysis. I could go into further detail about all of the technical complications of web scraping and how it is a behemoth task for software developers. Still, those details are not imperative to the implications it has. Let's talk about why web scraping is not the panacea it is positioned to be. Limited unit availabilityWhen listing apartment homes on an ILS, operators may deliberately withhold the number of units that appear online. This may be due to a limited number of units available at the property or, in the case of new development properties, the operator may want to create the appearance of low supply. Either way, if you are relying on web scraping, you are only getting the data that properties are making available. Leasing StatisticsLeasing statistics, such as occupancy, leased, and availability percentage is not......
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Don't Worry Too Much about Average Metro Rents – It's the Rents of Your Comps that You Really Want

Don't Worry Too Much about Average Metro Rents – It's the Rents of Your Comps that You Really Want
When it comes to apartment market data, it's important to make a distinction between "macro" and "micro."Macro data refers to statistics like a metro area's average rent or its occupancy rate. Micro data refers to the average rent or vacancy rate of a competitive set of communities. Think of it in terms of a photo – macro is the whole photo. You can see the entire picture but not the details. Micro data is like zooming in on that photo. You can now see things like a ribbon in someone’s hair or a flower just starting to bloom. When evaluating the performance of your apartment properties, it's helpful to consider both macro and micro statistics. But in the end, micro data – the more detailed, closer view data – will provide by far the most valuable, relevant insight.The Fallacy of Averages To be sure, reading third-party monthly or quarterly reports detailing a metro area's apartment macro data can give operators important context for their communities' performance. It's always good to understand the broader market in which your properties operate and to take in the numbers, insight and analysis regarding trends in your metro area.But here's the thing: in the end, when it's time to truly put the performance of your community into perspective, micro data eats macro data for breakfast. Put simply, if your property is located in say the booming Midtown area of Atlanta, you can't really evaluate your property's pricing based on the average rent in metro Atlanta. Even the ave......
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The Competition: Your secret pricing power

The Competition: Your secret pricing power
  When it comes to successfully pricing your apartment homes, it's critical to know who your competitors are and what rental rates they are charging.Unfortunately, though, too many operators don't have a true, clear-eyed understanding of their properties' comp sets. Below are some common mistakes property managers make when trying to determine who the competition is.• Assuming neighboring properties are comps. For sure, nearby communities – say, those within a one-mile radius – have a strong chance of being a competitor, but that's not always the case. Perhaps your community is a Class B asset, and the one down the street is a Class A or a Class C. Those may not be a comp. But the Class C across the street that is doing some value-add renovations, well, they may be another story. Visibility into property specifics like units, amenities and renovations can really help better determine what neighboring properties you really should be watching. • Assuming that communities with the same floorplan types are comps. It's tempting to assume that a nearby community in the same asset class as yours is definitely a comp if it offers the same floorplans as yours. But operators need to be more discerning than that.For instance, say that nearby community's one-bedroom units are only 450 square feet and yours are 900 square feet. A significant discrepancy between the size of your two-bedroom homes and theirs also exists. Should this property really be included in your comp set?• Not adjusting your comp set based on your c......
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Rental Data Delays Are Not OK

Rental Data Delays Are Not OK
  "Life moves pretty fast." So said the wise philosopher Ferris Bueller in the 1986 classic movie.I can't help but think of that line sometimes when thinking about apartment rental rates. They move pretty fast, too. In fact, they're changing on an almost daily basis.To make the most effective decisions regarding pricing and concessions, apartment communities need access to real-time rental rate data, or something extremely close to it.The problem is, though, many operators are relying on data that – especially in the lightning-fast world we live in now – seems downright out of date.Delayed DataOperators rely on a number of data points when trying to determine how rates for their communities compare to the competition. These data points include everything from market rent, concessions (upfront and/or amortized) for each unit type, occupancy and leased percentage, traffic and leases, amenities, etc. To help ease the process of gathering all these data points, operators sometimes turn to market surveys compiled by third-party research organizations. These surveys are usually conducted at the market level and then a regression analysis is performed to determine submarket data. While this methodology seems to offer the most robust and logical look at how an entire market or metro is performing there are two key areas of concern: 1) the survey data is, in the best-case scenario, 30 days old and 2) the surveys don't provide true insight into the surrounding submarket, let alone at the property/comp level. It is a common practice for research firms to collect mar......
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The Practical Power of Trial and Error

The Practical Power of Trial and Error
Ever wonder how powdered detergent is manufactured?  Of course you do!  Liquid detergent gets sprayed on to a wall where it dries, falls to the floor and is then gathered and packaged.  Pretty straightforward, right?  It turns out, however, that the way in which the liquid is sprayed greatly impacts the efficiency of the entire process.  The right type of nozzle becomes crucial.  Queue Unilever, a major manufacturer, who wanted to develop a new type of nozzle that would both provide a better spray pattern and reduce the likelihood of the nozzle getting blocked. What did they do?  They turned to the experts.  A team of mathematicians, physicists and engineers worked together to design the new nozzle, utilizing a wealth of data, knowledge and experience in fluid dynamics.  The end result was a beautiful new product that they were sure would be optimal.  It was a huge failure. Not intent to give up, Unilever decided to take a different approach.  They created 10 random variations of the existing nozzle, some bigger, some with grooves, some without, etc…  They took the one that performed the best, created 10 new variations, and repeated the process.  45 generations later they ended up with a totally unique nozzle design that both exceeded all their initial targets and baffled the experts.  With a little trial and error, the next generation in powdered detergent manufacturing was born. Why do we sometimes fear trial and error? Our industry is facing a growing number of complex business problems that aren't going t......
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Real Estate Technology Trends| What Technology Trends Are In-Store for Multifamily in 2014?

Real Estate Technology Trends| What Technology Trends Are In-Store for Multifamily in 2014?
According to an article published by industry insider Donald Davidoff for NAA (National Apartment Association), there are four key trends emerging in apartment technology that apartment executives need to be aware of. Customer Focused Business Intelligence While the quest to crunch “big data” continues on, the multifamily industry finds itself in the unique position of heading in the right direction. Techies everywhere are in the process of engineering new ways for their customers to use their property and revenue management data. And while the biggest hindrance to this effort is the resistance to wanting to invest in the analysts needed to access the data, more and more professionals are focusing on the possible benefits of analytics as the industry becomes more educated. Keep an eye out for solutions in 2014 to look at business intelligence being able to answer questions like, “Will our resident renew their lease?” or “Are our current residents really satisfied?” Looking for More Online Customer Feedback A satisfied resident really is the key to a successful community. That’s why there will be a growing fixation on things like customer feedback, especially when it comes to online ratings and reviews. After all, the statistics show that consumers are reading and taking to heart these types of peer-based reviews. The problem with multifamily apartment communities is that unlike a hotel the turnover is relatively limited to maybe once every six to twelve months. This means that the actual number of legitimate reviews for any one property is very limited. Look for the industry on the whole ......
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The 4 Types of Bad Data Getting in the Way of Multifamily Business Intelligence

The 4 Types of Bad Data Getting in the Way of Multifamily Business  Intelligence
So what’s all the buzz about business intelligence (BI), and just what does BI mean for apartment operators and managers, anyway? “Put simply, business intelligence is all of the things that enable you to make good, data-based decisions to improve business performance,” explained Glenn Thomas, Lead Software Development Manager, Business Intelligence at Property Solutions during an industry technology event last fall. With the multifamily industry increasingly looking toward the freedom provided by open, agile systems able to capitalize on the competitive opportunities revealed in strong data and reporting, Thomas outlined the four most common data challenges with multifamily software systems that traditionally have inhibited the ability to cultivate actionable BI: 1)                 Bad Data: Bad data includes duplicate files and data sets, corrupted files, and non-tied data sets that should logically be connected or relational. 2)                 Old Data: This is yesterday’s news, or worse, even older legacy data from systems, products, properties, and people no longer relevant (and therefore antagonistic) to your enterprise and your data management. 3)                 “Can’t Get There From Here” Data: Fixed reports and data sets that are not malleable enough for analysis and interaction offer little promise for efficient and actionable data analysis for multifamily strategists. 4)                  “Can’t Get There Quick Enough” Data: The red-headed step child of “Can’t Get There from Here” data is data that might be malleable or interactive, but is for a variety of reasons held captive from the data analyst, including data sets dependent on participation from other partners, vendors, or housed in disparate systems.  ......
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