Quality and condition are not the same. Quality refers to the quality of items, materials, and construction. When making appraisal adjustments, examine the quality of your data and remember that any quality ratings in your county records or MLS are not always reliable. Here are some basic things to consider regarding quality adjustment research and methodology.
Sources of data
A lot of the time, you’re using MLS data. You can also look at county records and get information from data providers. You are going to use shared databases, as well as your own observations and inspections—which are an excellent source. Photographs, too, are often priceless.
Quantity of data
So, you need to consider: How available is the data in your area? But most importantly, you want to take a look at the quantity of data. How many sales are you actually using in whatever method you determine?
The quantity is going to vary by method. In paired sales, you’ll likely be using a very limited number of sales. But with regression, you may use hundreds of sales.
Reliability of data
You need to consider: What is this data telling me? How reliable is that data? If you’re using county data, and you’re looking at quality ratings in there, how reliably do they rate properties? Should you even be using that data?
What are we trying to do with quality adjustment research?
We’re trying to do two things with quality adjustment research:
- Establish whether there is or is not a difference in contributory value from a particular feature.
- If there is, then what is the value difference?
When you’re determining these things, remember the following: Area matters. Type of property matters. You can’t just automatically apply conclusions to other areas and other property types. You also don’t want to assume that, just because someone else is telling you something about their market, that the same concept is going to be true for yours.
Reconcile your final adjustment rate
It’s a good idea to use multiple methods to determine your adjustment rate. And if you are using multiple methods (paired sales, regression, grouping) to derive that adjustment rate, you need to reconcile your final adjustment rate.
For example, if one method says 10% and another says 17%, you need to take a look at reconciling them. Maybe you can conclude it’s somewhere in the middle, or perhaps, for some reason, you’re going to the high end or the low end. You need to have some logical reconciliation for why you’re going up or down, or why you’re in the middle.
It’s important to apply logic to all of this. Just because one method tells you one thing doesn’t mean you worship that number and stop work. Look at the comps, look at your sales grid, and ask: Is this logical? Are you actually closing the gaps between the comps? Or is it spreading them out further? Is it just totally illogical in other ways?
Summarize your rationale
Finally, summarize your rationale within the report narrative.
Editor’s note: This post was originally published on April 21, 2020 and updated in May 2023.