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Conjoint Analysis Home buyingDuring my recent first time home buying experience I learned there are many, often competing, factors to consider.   My last blog discussed how I used Bracket™, a tournament-based analytic approach, to determine what homebuyers find most important when considering a home. My list of 13 items did not include standard house stats like # of bedrooms, # of baths, etc. To measure preference for those items I used a conjoint design.

I framed up the conjoint exercise by asking homebuyers to imagine they were shopping for a home and to assume it is located in their ideal location. Using our online panel of consumers, we showed recent or soon-to-be homebuyers 2 house listings side by side, plus an “I wouldn’t choose either of these” option. Each listing included the following:

        • Number of bedrooms: 1, 2, 3 or 4
        • Number of bathrooms: 1 full, 1 full/1 half, 2 full, 2 full/1 half or 3 full
        • House style: Single Family, Townhouse, Condominium, or Multi-Family
        • House condition: Move-in ready, Some work required or Gut job
        • Price: $150,000, $200,000, $250,000, $350,000 or $450,000

I felt a conjoint was best suited here, because in addition to importance, I wanted to see what trade-offs homebuyers were willing to make between these 5 items that are highly important in home buying. Are homebuyers willing to give up a bedroom to get the right price? Are they willing to do some sweat equity to get the number of bedrooms and/or bathrooms they want?

We found the top three most important factors are # of bedrooms, price and house condition. This made perfect sense to me as I would not consider any house with less than 3 bedrooms. Price and house condition were the next two key pieces. Was the house in my price range? How much work was needed? Did the price give me enough wiggle room for repairs? I was curious to see the play between price and house condition among the recent and soon-to-be homebuyers we interviewed.

Using the simulator I selected a 3 bedroom , 2 full baths, Single Family home. I picked 3 price points ($150,000, $300,000, $450,000) and then varied the house condition. Overall, homebuyers are less interested in a "gut job" compared to "move-in-ready". However, at the $150,000 price point, share of preference drops more drastically going from "move-in-ready/some work required" to "gut job" compared to higher price points.

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whats important homebuying market researchThe weather is starting to warm up and more of us are venturing outside, myself included. Walking my dog around the neighborhood I’ve noticed a number of for-sale signs and it reminds me of my own recent home buying experience. It was exciting and at the same time stressful. Once I made the decision to buy I started watching all the home buying shows and attending open houses to figure out my list of must-haves and nice to haves. I wondered how my list stacked up against others who went through or are going through the home buying process.

Using our online panel of consumers, I employed TRC’s proprietary Bracket™ exercise to find out what homebuyers find most important when considering buying a home. Bracket™ is a tournament-based analytic approach to understanding priorities. For each participant, Bracket™ randomly assigns the items being evaluated into pairs. Participants choose the winning item from each pair; that item moves on to the next round. Rounds continue until there is one “winner” per participant. Bracket™ uses this information to prioritize the remaining items, and calculate the relative distance between them.

I created a list of 13 things to consider. I didn’t include standard house stats: # of bedrooms, # of baths, etc. as I tested those separately using a conjoint analysis (my next blog will dive into what I did there).

Proximity to work

Proximity to family

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Catalog Cover TestingVery few clients will go to market with a new concept without some form of market research to test it first. Others will use some real world substitutes (such as A/B Mail tests) to accomplish the same end. No one would argue against the effectiveness of things like this...they provide a scientific basis for making the right decision. Why is it then that in early stage decision-making science is often replaced with their gut?

Consider this...an innovation department cooks up a dozen or more ideas for new or improved products and services. At this point they are nothing more than ideas with perhaps some crude mock-ups to go along with them. Doing full out concept testing would be costly for this number of ideas and a real world test is certainly not in the cards. Instead, a "team" which might include product managers, marketing folks, researchers and even some of the innovation people who came up with the concepts are brought together to wean the ideas down to a more manageable level.

The team carefully evaluates each concept, perhaps ranks them and provides their thinking on why they liked certain ones. These "independent' evaluations are tallied and the dozen concepts are reduced to two or three. These two or three are then developed further and put through a more rigorous and costly process - in-market testing. The concept or concepts that score best in this process are then launched to the entire market.

This process produces a result, but also some level of doubt. Perhaps the concept that the team thought was best scored badly in the more rigorous research or the winning concept just didn't perform as well as the team thought it would. Does anyone wonder if perhaps some of the ideas that the team weaned out might have performed even better than the "winners" they picked? What opportunities might have been lost if the best ideas were left on the drawing board?

The initial weaning process is susceptible to various forms of error including group think. The less rigorous process is used not because it is seen as best, but because the rigorous methods normally used are too costly to employ on a large list of items. Does that mean going with your gut is the only option?

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Market Research Event conjoint AnalysisLast week we held an event in New York in which Mark Broadie from Columbia University talked about his book “Every Shot Counts”. The talk and the book detail his analysis of a very large and complex data set…specifically the “ShotLine” data collected for over a decade by the PGA. It details every shot taken by every pro at every PGA tournament. He was able to use it to challenge some long held assumptions about golf…such as “Do you drive for show and putt for dough?”

On the surface the data set was not easy to work with. Sure it had numbers like how long the hole was, how far the shot went, how far it was from the hole and so on. It also had data like whether it ended up in the fairway, on the green, in the rough, in a trap or the dreaded out of bounds. Every pro has a different set of skills and there were a surprising range of abilities even in this set, but he added the same data on tens of thousands of amateur golfers of various skill levels. So how can anyone make sense of such a wide range of data and do it in a way that the amateur who scores 100 can be compared to the pro who frequently scores n the 60’-s?

You might be tempted to say that he would use a regression analysis, but he did not. You might assume he used Hierarchical Bayesian estimation as it has become more commonplace (it drives discrete choice conjoint, Max Diff and our own Bracket™), he didn’t use it here either.

Instead, he used simple arithmetic. No HB, no calculus, no Greek letters, just simple addition, subtraction, multiplication and division. At the base level, he simply averaged similar scores. Specifically he determined how many strokes it took on average for players to go from where they were to the hole. These averages were further broken down to account for where the ball started (not just distance, but rough, sand, fairway, etc) and how good the golfer was.

These simple averages allow him to answer any number of “what if” questions. For example, he can see on average how many strokes are saved by going an extra 50 yards off the tee (which turns out to be more than for being better at putting). He can also show that in fact neither driving nor putting is as important as the approach shot (the last full swing before putting the ball on the green). The ability to put the ball close to the hole on this shot is the biggest factor in scoring low.

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  • market research philadelphia farmersA recent post on my Facebook timeline boasted that Lansdale Farmers Market was voted the Best of Montgomery County, PA two years in a row. That’s the market I patronize, and I’d like to feel a bit of pride for it. But I’m a researcher and I know better.

Lansdale Farmers Market is a nice little market in the Philadelphia outskirts, but is it truly the best in the entire county? Possibly, but you can’t tell from this poll. Lansdale Farmers Market solicited my participation by directing me to a site that would register my vote for them (Heaven only knows how much personal information “The Happening List” gains access to).  I’m sure that the other farmers markets solicited their voters in the same or similar ways. This amounts to little more than a popularity contest. Therefore, the only “best” that my market can claim is that it is the best in the county at getting its patrons to vote for it.

But if you have more patrons voting for you, shouldn’t that mean that you truly are the best? Not necessarily. It’s possible that the “best” market serves a smaller geographic area, doesn’t maintain a customer list, or isn’t as good at using social media, to name a few.

  • A legitimate research poll would seek to overcome these biases. So what are the markers of a legitimate research poll? Here are a few:
  1. You’re solicited by a neutral third party. Sometimes the survey sponsors identify themselves up front and that’s okay. But usually if a competitive assessment is being conducted, the sponsor remains anonymous so as not to bias the results.
  2. You’re given competitive choices, not just a plea to “vote for me”.  
  3. You may not be able to tell this, but there should be some attempt to uphold scientific sampling rigor. For example, if the only people included in the farmers market survey were residents of Lansdale, you could see how the sampling method would introduce an insurmountable bias.

The market opens for the summer season in a few weeks, and you can bet that I’ll be there. But I won’t stop to admire the inevitable banner touting their victory.

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