Salary Expectations

One of the questions frequently asked of the National Association of REALTORS®’ Strategic Association Management team is, “How do I know if I’m paying my staff in line with the market? How do I benchmark pay?”

Compensation is a science, and the best way to respond to this concern is to have a human resources professional conduct a market analysis. It’s more than just gathering pay ranges from a survey conducted by a survey firm. There’s a complex process for gathering and verifying data from multiple organizations within a given industry.

Here’s where it can get a little tricky. Sometimes, surveys are collected on salary and benefits but are not conducted by compensation firms or subject matter experts. These surveys often provide results similar to, say, a member satisfaction survey. As a result, they often include inflated salary data because the results are self-reported, and no further analysis is done.

Right now, you might be thinking, “Sounds good to me,” but inflated salaries tend to price people out of the market, making it harder for them to find other employment when the need arises. (By the way, this can also happen with inflated titles.) These types of surveys also tend to produce extremely wide ranges of salary information, making it hard to establish a true range.

In the field of compensation, the subject matter experts also call the data they use a “survey,” causing further confusion. However, compensation or salary surveys conducted by experts are a compilation of wage data collected from organizations for jobs perceived as comparable to each other. This data also undergoes rigorous analysis.

Compensation surveys conducted by experts begin with a job-matching process using benchmark job descriptions provided by the surveyor against internal job descriptions. Job descriptions are used for this purpose rather than titles because titles can be misleading through being inflated, deflated, or conflated.

Setting compensation is more than just gathering pay ranges from a survey conducted by a survey firm.

When data is gathered, it may be weighted or unweighted before arriving at the mean or midpoint. Salary data is regressed—which is a method of testing for possible compensation disparity within an organization. A regression analysis is a statistical process that addresses possible disparities—for example, years of experience, performance, skills, etc. The data is also aged to a common date by adjusting the salaries from the date of the survey to the present.

All this data is then averaged to provide a market rate. This is why compensation matters are best left to professionals in compensation, and it’s also why these surveys are so expensive. Some compensation firms will publish their entire results, and some will only provide salary information for one job at a time. Most often, organizations don’t have the budget to purchase the full salary survey.

In the event of a legal matter, an organization may need to defend its salary structures. The best way to do this is by using survey data provided by experts. It isn’t enough to say, “This is the range offered based on the budget,” or to say that you pulled data from a survey that didn’t go through rigorous analysis.

So, now you have learned more than you ever wanted to know about determining compensation, and hopefully you understand why I recommend using an expert. And when your staff members complain about their salary, you can wonderfully overwhelm them with how much you know about compensation and tell them precisely how their salary is determined, which should put an end to any further complaints. This applies to the board as well. As much as we understand governance, we recognize that sometimes board members get very curious as to how much staff is paid. Using expert data resources provides you with the opportunity to explain how the data is scrubbed and assure them that staff is paid appropriately without having to reveal actual amounts.

Do you have an HR question? Send it to Carole Kaptur confidentially at ckaptur@nar.realtor.