Does Big Data equal Big Value?

Is Big Data a hyped up buzz word? Can Big Data help my business? Is it worth investing in Big Data tools? These are some of the questions I get from people I meet at my Alma Mater’s Big Data round table group.  Of course my consultant answer is “yes and no” and then I go into a fifteen minute soliloquy about the pros and cons of Big Data at the end of which my listeners are utterly confused. Well to clear some confusion I wanted to put my thoughts down in writing.

Honestly, most of the business leaders I speak to say they are implementing Big Data tools in name only with no real expectations of getting big value out of them. In fact a recent Gartner survey found that only 44% of the 284 businesses surveyed had invested in Hadoop (a popular Big Data storage technology) or had plans to do so within the next two years. The survey also found that the businesses which did invest in Hadoop are not exactly championing for substantial adoption of Hadoop in the next 24 months. I’m sure Big Data vendors and people living in the Silicon Valley bubble hate me for pouring cold water over the excitement around Big Data but I believe this is where the real world is right now. While businesses are slowly getting onboard with the storing of vast amounts of data, they are still struggling to find good use cases that deliver tangible results to justify the investments in Big Data technologies.

In the quest for finding value out of Big Data I would like to add my voice and suggest a few Big Data use cases to increase revenue and profits in the Real Estate industry. This is an industry which is near and dear to my heart, not only because I have worked in this industry for a long time but also because I made some unholy sums of money flipping houses (well actually just one house and that too because I got lucky). Within the Real Estate industry I’m especially interested in the Multi-family/Apartment Housing segment. As per a 2013 study conducted by National Multifamily Housing Council (NMHC) and the National Apartment Association (NAA), 38.1 million people live in 20.1 million apartment homes contributing $1.3 Trillion to the US economy and supporting 12.3 million jobs. Those are staggering numbers and any improvement in the efficiency of this industry using Big Data could result in substantial gains. Big Data is being successfully used in the Hotel industry for some time now but the same cannot be said of the multi-family industry. This is partly due to the fragmented nature of this industry with lots of small mom and pop outfits. In fact, if you were to add up the portfolios of all the public REITs, you’d have less than 10% market share of the nation’s apartment stock. But I believe the big players have enough data internally which can be blended with externally available macro economic data to build useful analytical models. In fact a new startup Rentlytics recently raised big money to do exactly this, although I believe they don’t go far enough.

In general the key question any revenue management/predictive analytics system should answer is “how much should we charge for the next vacant unit”. Getting this answer wrong could result in leaving money on the table (under pricing) or losing a sale resulting in a vacant unit (over pricing). You can extract the best margin when you price as close as possible to the customer’s “willingness to pay”. The customer’s willingness to pay depends on a lot of attributes which includes their own financial position and the local real estate market. We can get an insight into these aspects using the below data sources.

1. Local market conditions: Competitor’s prices, Employment statistics (BLS), new constructions, Crime statistics, Case Shiller Index etc.
2. Customer Demand: Online Website hits, Phone Inquiries, Walk Ins/Visits, customer interactions as recorded in CRM/Sales systems.
3. Supply side information: Occupancy/Vacancy by Unit Type that is up to date
4. People Analytics: HR data to catch issues related to property management and maintenance
5. Revenue and Expense: Accounting data to track the results.

One method of increasing revenue I tried while I was working at one of the largest senior living companies was to predict the likelihood of a prospective resident using customer interactions as recorded in the custom sales system. Every phone conversation was documented in the system which provided for a great insight into the kind of conversations that led to converting a prospect into a resident. Providing the right incentives and discounts to the right customer can increase the conversion rate and consequently the revenue. Also, we can analyze the past historical data on incentives, occupancy, and duration of resident stay to come up with optimum incentives for a particular prospective resident. Optimizing the incentives and discounts can increase profits. A realistic goal would be 5-10% growth in revenue as a result of these efforts. As with any new technology the key is to start small and secure early wins which will help with its adoption throughout the organization.