diff --git a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_12_2011_relative_differences_between_methodologies-ranking-percentile.ipynb b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_12_2011_relative_differences_between_methodologies-ranking-percentile.ipynb
index 9265275c..11222854 100644
--- a/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_12_2011_relative_differences_between_methodologies-ranking-percentile.ipynb
+++ b/data/data-pipeline/data_pipeline/ipython/hud_eda_se_12_12_2011_relative_differences_between_methodologies-ranking-percentile.ipynb
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -1104,6 +1104,76 @@
     "       'current_methodology_denominator', 'current_methodology_percent']]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### First notice here that **T8_est1** and **current_methodology_denominator** should represent same or similar aggregates. In general, we cen see that the current computation performed results in a differerntial that undercounts the total occupied and rental households."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/0m/ppxy6yr56jx1mk52p_9sf2sw0000gn/T/ipykernel_69620/1675757007.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  final_df[\"differences_aggregate_denominator\"] = (\n"
+     ]
+    }
+   ],
+   "source": [
+    "final_df[\"differences_aggregate_denominator\"] = (\n",
+    "    final_df[\"current_methodology_denominator\"] - final_df[\"T8_est1\"] \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'Distribution of differences between aggregate totals that normalizes tabulation of poverty households'}, xlabel='Aggregate differences in total owner and renter occupied low-income households < 80%', ylabel='Relative Frequency in Support'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.title('Distribution of differences between aggregate totals that normalizes tabulation of poverty households')\n",
+    "# Set x-axis label\n",
+    "plt.xlabel('Aggregate differences in total owner and renter occupied low-income households < 80%')\n",
+    "# Set y-axis label\n",
+    "plt.ylabel('Relative Frequency in Support')\n",
+    "\n",
+    "sns.histplot(final_df[\"differences_aggregate_denominator\"])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},