A Short-Hand Solution to Finding Comparables

One of my most instructive memories and insights into local government came on my third day in my first job. We had some minor flooding issues (water sitting on roads with some homes most or less fortified against a medieval army but dry inside and accessible), and the Administrator swung by my desk coming out of a response meeting. He took a big swig of coffee and said, "Well, since you're new both here and to the field, let me be the first to ask you a question you'll hear about a million times before you're where I am - what are the comparables doing about clean-up?" True to his word, between being directly asked what our comparables do and thinking it to myself, I'm probably approaching 100,000 times. To be sure, it's an entirely reasonable question, but the methods used to answer who your comparables can range anywhere from well-reasoned to entire guess work.

As we all know, everyone looks to comparable organizations for any number of things. Both private and public sector organizations look to those they consider either competitors or fundamentally similar for benchmarks and ideas. However, due both to a lack of traditional market competition and freedom of information, public sector organizations are inclined to go so far as to borrow language directly from one another for policies and volunteer extraordinarily large amounts of management and operating information. Therefore, because the materials are so readily available, the quest for comparables becomes so second nature for public administrators that it enters into most major decisions.

Again, that's not the problem - the issue is how to differentiate between useful and extraneous (or, in the most extreme circumstances, harmful) information. And that's where the problem becomes critical. In some cases, the exercise is unnecessary. For example, when discussing flood response, comparability was more of an intellectual exercise because, we were all going to respond in a similar manner regardless of what we had in common. However, when we get to a subject like collective bargaining, comparability is a key issue in determining what's affordable for something like a COLA. Based on my experiences and watching how others have gone about determining comparables, any one of the following reasoning measures can be used to determine who to go with for comparison:

1.  "Whoever We Have Always Used": A classic response is that the comparables are whoever you have always looked to. These other communities may or may not be truly comparable, but without additional information, it's impossible to tell.
2. Proximity: I've seen a lot of communities look around their neighborhoods to find comparables. In some respects, that's not a bad response - after all, it's likely that some essential factors are similar. However, we cannot say that Arlington, VA is comparable to Washington DC simply because of their proximity.
3. One or More Key Similarities: This is a particularly tempting method to follow. If there is a key similarity between two communities, it may be simple to say that they are comparable (for example, two communities may both have major universities). However, while some factors are likely the same, nobody would argue that Iowa City, IA and Ann Arbor, MI are fundamentally similar because they both have Big 10 Schools.
4. Assessed Valuation: Another common piece of reasoning is that assessed valuations is a good shorthand way to determine comparables. I agree with this to an extent - assessed valuation is a decent indicator when the distribution of residential, commercial and industrial valuations are considered but not the total figure it and of itself. A town with a ton of residential valuation might look comparable to a city with lots of industrial valuation otherwise.
5. Community Relationships: This normally occurs where two communities have a particularly close relationship (either the communities or their leaders do) and end up becoming de facto comparables because they immediately consult each other first when they need external information. I wrote my grad school capstone on a related topic and have noticed that my particular friendships with others in the field have lead to the policies of the communities they work for influencing my employer's policies (because I borrow from them) and vice versa. This is not problematic in and of itself, but if the two communities are dramatically different, this wouldn't be advisable.
6. Vanity: Many times, communities will identify the communities they wish to aspire to or be compared to as comparables. The reasoning is simple: if these communities are "better," then we can become "better" by following their example. However, this is the biggest trap of all - odds are that these "better" communities came to be under different circumstances and borrowing heavily from them without understanding your underlying differences is a recipe for trouble.

So, how then can we go about determining comparables? When forced to determine an "objective" way of doing this for a collective bargaining agreement a few years ago, I spent a few hours and mapped out an index which compiles a variety of unrelated information and attempts to find the best match. The formula is below (warning - some math follows):

X = ∑ y
y = 1- ((Z-A)/B)

For those not up-to-date with mathematical summation, the formula above basically says that X is the sum of all y variables. Each y variable is expressed as the Z variable minus A dividedby B then subtracted from one. It all sounds complicated, but it practice, it's relatively simple. Before we proceed though, some explanation on why y is used instead of a set figure. This is because, depending on your purposes, any number of variables may be used. I've used between three and seven at a time, so I leave the number open.

Let's see how this formula works out in practice. Let's say that I manage a town of 50,000 people with $1,000,000 in assessed valuation. I'm looking for the most generalized comparables possible. Here's the few that I have immediately identified:

• Town A: 65,000 population, $900,000 in assessed valuation.
• Town B: 80,000 population, $1,100,000 in assessed valuation.
• Town C: 55,000 population, $800,000 in assessed valuation.
• Town D: 35,000 population, $950,000 in assessed valuation.

Clearly, each of these is not a perfect match, but the question is how do we determine the best match. Here is what I propose in a step-by-step format:

1. Separate each variable to keep the data consistent - i.e. put all population figures in one sheet.
2. Subtract each figure in that particular variable set by your community's number (explanation will follow at the end).
3. Convert any negative numbers to positive numbers.
4. Divide each figure by the largest number to get a percentage.
5. Subtract that percentage from 1.
6. Repeat these steps with each variable identified as an indicator of comparability and finally add each town's figures for a final index sum.

What we are trying to accomplish here is a shorthand way of showing general comparability. As that is the case, traditional means of summarizing data (such as averages) are irrelevant because they do not make comparisons of one number to the set. Therefore, what we are trying to do is measure variation. In each case, subtracting your town's figure establishes it as 0 and the basis of comparison. Essentially, what happens then is the most disparate community is treated as the range and each community's figure is plotted along that range. That plotting is the foundation of the index. Below is how the math works for the example I've given.

Initial Data (raw data):

	Pop	EAV
Your Town	50000	1000000
A	65000	900000
B	80000	1100000
C	55000	800000
D	35000	950000

First Treatment (Your town's data subtracted from all figures and all figures converted to positive numbers):

	Pop	EAV
Your Town	0	0
A	15000	100000
B	30000	100000
C	5000	200000
D	15000	50000

Second Treatment (divide each figure by the most disparate - for example, all population figures are divided by 30000):

	Pop	EAV
Your town	0	0
A	0.5	0.5
B	1	0.5
C	0.1666666667	1
D	0.5	0.25

Final Treatment (subtract each figure from 1 and add all figures):

	Pop	EAV	Final Score
Your Town	1	1	2
A	0.5	0.5	1
B	0	0.5	0.5
C	0.8333333333	0	0.8333333333
D	0.5	0.75	1.25

As you can see, Town D is considered the most generally comparable based on these factors, though it may also be worth while looking at Town A. 

You may be asking if I've used up to seven variables before then what should you be looking to. Here's a list of potential items to use for comparability:

• Population
• Assessed Valuation
• General Fund Amount
• Property or Sales Tax as a Relative Portion of the General Fund
• Home-rule status (this is an exception to the rule - since a community is either home-rule or it isn't, I generally just use 1 and 0 for scores)
• Ratios of Assessed Valuation (residential vs. commercial vs. industrial, etc.)
• Number of emergency calls (for police and fire)
• Lane miles (for engineering or public works)
• Miscellaneous emergency date (number of vehicle accidents, ALS/BLS figures, etc.)

Before jumping into this model fully, just two things to be aware of: (i) be sure you are comparing apples to apples and (ii) recognize how the choice of communities used will impact your figures. In regards to the comparison, a common problem is that there is some variation in what does and does not get measured (for example, if you are looking at requests for service, be sure that all comparison communities use similar systems for tracking). Finally, realize that including a number of disparate communities will skew your numbers somewhat. However, because we are looking at relativity, most areas should work out fine.

In short, I hope this is a useful tool for you going forward. Keep in mind that multiple different communities may be ideal comparables depending on what you're looking at, and you should always think through these issues each time you go out to review a major issue. What are your thoughts and what are you looking at comparables for?