Which somewhat goes back to my original post (seems like years ago!

).
If the adjustment is supported, then it is supported. If the adjustment is supported, then it might support adjustments across the board.
If the adjustment is not supported, then it is not supported. If independent analysis by the reviewer concludes something else, then that independent analysis becomes the reviewer's basis for (a) disagreement with the analysis and (b) possibly a different value conclusion if an appraisal-review value is part of the review SOW.
What is not a basis is "I wouldn't do it this way, so another shouldn't" when all the recognized texts regarding market condition adjustments state the same thing: If they are supported, apply them; and if they are supported for all the sales in the grid, apply them to all the sales in the grid.
Now, separate from this specific appraisal under review, but applicable in general:
I find it hard to believe (but not impossible that such would exist) that a single data point at the front-end of the 1004mc would be such an outlier that it would impact the market conditions analysis. It would seem to me if that is the case, there simply isn't enough data in the entire analysis to make credible market-trend conclusions (and, I think that is one of the biggest complaints most raise concerning the 1004mc; by itself, it isn't always reliable). Further, it may not be a true comparable in the first place. I do think excluding it would be the appropriate thing to do if it was a flip/renovation, etc.
Second, if one is going to rely solely on the 1004mc for market conditions, one better understand its weaknesses.
I've posted this one before- I hope someone can point out where I have a flaw in this example because I cannot see it:
There are three buckets that contain data from different-sized periods and refine the trend to a data-point that reflects a median price. It may not be (and likely rarely is) the annualized price-trend many use it for.
Let's make this easy: Let's assume the distribution of sales is equal along the timeline within each bucket's period (and how often does that occur with a small data set?):
The first bucket is six-months (prior 7-12 months). Assume it has 9 sales and the median price is $450k. That means the median price is sale #5 as ranked by price. But it reflects all sales within that period, so the point-in-time may not be 12 months ago but on average would be approximately at the end of month 9.
The second buck is 3 months (prior 4-6 months). Assume it has 5 sales and the median price is $460k. That means the median price is sale #3 as ranked by price. But it reflects all sales within that period, so the point-in-time may not be 6 months ago but approximately 5.5 months ago.
The last bucket is the last 3-months (current to 3-months. Assume it has 5 sales and the median price is $465k. That means the median price is sale #3 as ranked by price. But it reflects all sales within that period, so the point-in-time may not be current but 1.5 months ago.
Here is how that data lays out:
9 months ago $450k.
5.5 months ago $460k.
1.5 months ago $465k.
What is the annualized appreciation? Most would say it started at $450k and ended at $465k, so that is 3.3%/year.
But what it means if sales are evenly distributed along the time line is, The median 9 months ago is $450k, the median 1.5 months ago is $465k. That is a $15k increase in 7.5 months; if you annualized that, you'll get something above 5%. And this assumes the data is evenly distributed; load the data toward month 7 in the first bucket and month 3 in the last bucket, and data skews to a higher appreciation rate.
My point? I wouldn't use this as my sole basis for making market-condition adjustments; as an originator or as a reviewer.