Some Assembly Required

This article is featured in the 2025 edition of the Appraisal Buzz Magazine. Some other features in our magazine include funny Buzztoon comics, industry trends, as well as crazy stories from appraisers and readers like you! Read all these articles and more in the latest edition HERE. If you want to make sure you are receiving the print version of the Appraisal Buzz magazine in your mailbox, sign up HERE.

Remember the first time you bought something from IKEA? You probably cursed under your breath when you opened the box to find 300 pieces, with a single Allen wrench to put it all together. Somehow, you transformed that pile of metal into a swiveling desk chair or a futon, using only a simple instruction booklet and a tiny, L-shaped tool.

I wish there were a single tool and one-page instruction manual to transform hundreds of pieces of information into a reliable and credible opinion of value. But sadly, no such appraisal Allen wrench exists.

At this point, you’re probably expecting me to advocate for learning more tools. While learning more tools is good, what I want to talk about instead is using various tools collectively rather than using each one in isolation. Often, appraisers think in terms of a single, best tool for a problem, such as supporting adjustments. Debates rage about which tool is best, with one appraiser solving every problem with paired sales versus another who always deploys regression.

Many appraisers have expanded their tool kits, learning to apply different tools in different circumstances. An example would be an appraiser deciding that depreciated cost is best when adjusting for a detached garage, while grouped pairs is best for an adverse view, and regression is best for gross living area. However, in each case the appraiser is still using a single tool to solve each problem.

What can work even better is combining tools, much in the same way that you might use a hammer and chisel together to accomplish a task that neither does as well on its own. To use the tools to best effect, order matters. For example, in masonry or woodworking, using the hammer to strike the chisel works very well, whereas using the chisel to strike the hammer just makes a mess.

Consider what it might look like to combine the tools of theory, logic, experience, cost, and sales data analysis (itself, comprised of many tools) to a solar PV system on the roof of a residential property. The ordering of those tools was intentional, beginning with theory and ending with sales data. That may seem counterintuitive, but data by itself is a poor storyteller. (In a recent presentation, I showed how the same set of data could indicate a GLA adjustment from $110/sf to $190/sf, depending on how it was analyzed.) So, it’s best to let other tools inform your approach to the sales data, beginning with theory.

Theory

Theory can offer you a framework that governs what considerations and approaches will work best for your assignment. For our solar example, the theory of anticipation could lead you to consider cost savings as a cash flow which could be capitalized. The theory of substitution could lead you to consider how alternative substitute investments might impact that rate of capitalization.

Logic

Next, let theory guide you toward empirical evidence that would point to some logical inferences. For example, you might examine an aerial image to see how many solar PV systems are present in the area. Logic would tell you that every PV system you see represents a market participant who decided that the benefit of that system exceeded its cost. If you notice that most homes in the market do not have a solar PV system, you can conclude that most homeowners feel that investing in solar does not pay off. That might be helpful information to return to as you consider the cost. You might also turn your thoughts back to theory and consider the implications of a niche market.

Experience

Although appraisers who cite “experience” as adjustment support are regularly derided in online forums, it shouldn’t be diminished when combined with other tools. What you’ve observed during your career can provide valuable context. Experience gives you permission to call a good initial guess about a value outcome an “educated” one, bolstered by countless observations over time and in multiple markets — as long as you know full well that a hypothesis formed by experience can still be disproved once it encounters new data.

In our solar example, you might have noticed that income capitalization did seem to be an incentive, creating some demand for the amenity, but that you’ve typically observed it to be superadequate. That experience falls in line with the logic and fits nicely into the theory of functional loss through superadequacy. If you have experience discussing this amenity with active agents in this market, that can also yield valuable information: perhaps agents consistently reported that even buyers who opted for solar power did not like street-facing panels. Those conversations can inform how you filter data later to consider homes with street-facing panels separately, so they don’t cloud your results.

Cost

While we’ve all heard that cost doesn’t equal value, the fact remains that cost can heavily influence value. In this solar test case, you can consider cost in conjunction with logic, theory, and experience. Logic and theory suggest that in the wider market, there’s likely some significant functional loss; thus, the contributory value should be less than the cost. You might consider the possibility of a market niche where functional loss may not exist, but you also know that the theory of substitution is likely going to cap the value at the cost, even in this market niche.

You could conclude this from the fact that there are many active solar installers, and buyers would be unlikely to pay more for a home with a system when they could buy any other home and install a new system themselves.

Sales Data

Let’s save data analysis for last.

If your observations are to truly reflect the market, you must consider sales data. But by the time you approach the sales data, those other tools have already given you a wealth of perspective and knowledge that’ll inform how you analyze the data.

In the solar example, logic and experience can guide how you search for and filter the data, including adding front-facing panels as a search criterion. You’ll likely consider both cost and income as potential independent variables when analyzing the sales data for contributory value.

Then, let experience and logic help you identify outliers in the data set. You might consider paired sales within a smaller data set as better indicators of the market niche, rather than using regression on a larger data set. 

Conclusion

Abraham Lincoln once said, “Give me six hours to chop down a tree, and I will spend the first four sharpening the axe.” I think that Lincoln would also have found that after chopping down that first tree, the axe was still sharp enough for the next one. Appraisers will find the same thing. The solar PV example I gave was a simplified real-world scenario I encountered in my practice. Sharpening that axe took a while, but now I can solve solar PV problems like that one more quickly and efficiently. 

While taking the extra effort to select the best tools in the best order might sound like a headache, it’ll save you time and trouble in the long run. You can use formulas and algorithms to choose the right tool combination, then replicate that process to produce credible results which are both better and faster. Is some assembly required? Yes, but I promise you: it’s worth it.