I have used the State of Connecticut Real Estate Sales data at data.gov
Metadata:
url: https://data.ct.gov/api/views/5mzw-sjtu/rows.csv?accessType=DOWNLOAD
Columns: 'Serial Number', 'List Year', 'Date Recorded', 'Town', 'Address',
       'Assessed Value', 'Sale Amount', 'Sales Ratio', 'Property Type',
       'Residential Type', 'Non Use Code', 'Assessor Remarks', 'OPM remarks',
       'Location'
Relationships:
'Date'[Date] ---> 'Real Estate Sales'[Date Recorded]

Below is the DAX code for the measures:
[Deal Size]
[Deal Size Last Year]
[Change %]
[Most Frequent Change %]

Warning: 
1.When doing a similar calculation as part of analysis,
rounding of the values that we find the frequency of, should
be done as otherwise values which are practically the same 
will not be counted together!
2.If using the Real estate sales data mentioned above, you
may come to a point where you may have errors thrown from power bi
due to blank values in the date column - pandas is the answer.

from pandas import read_csv,DataFrame,Series
realEstateSalesData: DataFrame= read_csv("Real_Estate_Sales_2001-2023_GL.csv")
print(realEstateSalesData.columns)
dateRecordedWithValue: Series = realEstateSalesData['Date Recorded'].notna()
realEstateSalesData[dateRecordedWithValue].to_csv("Real_Estate_Sales_2001-2023_GL_NoNa.csv")

Below are the DAX measures:

[Deal Size] = SUM(
  'Real_Estate_Sales_2001-2023_GL_NoNa'[Sale Amount])

[Deal Size Last Year] = CALCULATE(
      [Deal Size],
      SAMEPERIODLASTYEAR('Calendar'[date]))

[Change %] = DIVIDE(
      [Deal Size] - [Deal Size Last Year],[Deal  Size Last Year],10000000000)

[Most Frequent Change %] = 
  VAR _valueTable =
  ADDCOLUMNS(
      'Calendar',
      "Single Day Change",
      [Change %] -- <-- Rounding could be done at this point
  )
  VAR _frequencyTable =
  GROUPBY(
      _valueTable,
      [Single Day Change],
      "Frequency",
      COUNTX(CURRENTGROUP(),[Single Day Change])
  )
  VAR _mostFrequentValue = TOPN(
      1,
     _frequencyTable,
      [Frequency], --column
      DESC
  )
  return MINX(_mostFrequentValue,[Single Day Change])