Measure to average a pivot-table column in a Matrix with errors treated as blanks?

Greg_Deckler 

I've tried both the way you suggested and my way, and the Matrix output is identical under both versions.  

VAR OUTPUT_NEW = 


        AVERAGEX( 
            ADDCOLUMNS (
                SUMMARIZE (
                    ALLSELECTED ('Helivalues Transaction History'[Sale Year]),
                    'Helivalues Transaction History'[Sale Year],
                    "SUM", SUM ( 'Helivalues Transaction History'[Year of Build] )
                ),
                "__Value", [______A - workaround until you figure out the FiNAL PIECE - Depreciation Volatility Observed]
            )  ,
            [__Value]
        )

VAR OUTPUT_OLD = 
 
        AVERAGEX( 
            ADDCOLUMNS (
                SUMMARIZE (
                    ALLSELECTED ('Helivalues Transaction History'[Sale Year]),
                    'Helivalues Transaction History'[Sale Year],
                    "SUM", SUM ( 'Helivalues Transaction History'[Year of Build] )
                ),
                "Percent", CALCULATE ( SUM ( 'Helivalues Transaction History'[Year of Build] ) )  
            ),
            [______A - workaround until you figure out the FiNAL PIECE - Depreciation Volatility Observed]
        )

RETURN

Greg_Deckler 

Some progress here.  New PBIX file: https://drive.google.com/file/d/1pG3ua4BOWyIJhhh4dOwG9ac8UPWqUsYo/view?usp=sharing

By changing the first [measure] to the following by adding ALLNOBLANKROW first thing after summarize

VAR DEPRECIATION_VOL_OBSERVED =
        
            STDEVX.S(
                SUMMARIZE (
                        ALLNOBLANKROW('Helivalues Transaction History'),
                        'Helivalues Transaction History'[Sale Year]
                    )
                , [Average Annualized Price change (weighted by Model Year Units)]
            )

I get **a** sub-total rather than NaN.  What that subtotal represents isn't entirely apparent, and isn't dynamic across the pivoted columns.  It's summarized as 5.76% for each column and the grand total, rather than the average of what I perceive to be the available data points.