feat: add for sale listings and update new constructions notebook

processors/process_for_sale_listings.ipynb

@@ -0,0 +1,770 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = \"../data\"\n",
+    "PROCESSED_DIR = \"../processed/\"\n",
+    "FACET_DIR = \"for_sale_listings/\"\n",
+    "FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)\n",
+    "FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "processing Metro_new_pending_uc_sfrcondo_sm_month.csv\n",
+      "processing Metro_invt_fs_uc_sfrcondo_week.csv\n",
+      "processing Metro_mlp_uc_sfrcondo_week.csv\n",
+      "processing Metro_invt_fs_uc_sfr_month.csv\n",
+      "processing Metro_mlp_uc_sfr_sm_month.csv\n",
+      "processing Metro_new_pending_uc_sfrcondo_month.csv\n",
+      "processing Metro_mlp_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_invt_fs_uc_sfrcondo_month.csv\n",
+      "processing Metro_mlp_uc_sfr_sm_week.csv\n",
+      "processing Metro_mlp_uc_sfrcondo_month.csv\n",
+      "processing Metro_new_pending_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_invt_fs_uc_sfr_sm_week.csv\n",
+      "processing Metro_invt_fs_uc_sfr_sm_month.csv\n",
+      "processing Metro_mlp_uc_sfr_month.csv\n",
+      "processing Metro_new_listings_uc_sfrcondo_week.csv\n",
+      "processing Metro_mlp_uc_sfrcondo_sm_month.csv\n",
+      "processing Metro_invt_fs_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_new_listings_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_new_listings_uc_sfrcondo_month.csv\n",
+      "processing Metro_new_pending_uc_sfrcondo_week.csv\n",
+      "processing Metro_invt_fs_uc_sfr_week.csv\n",
+      "processing Metro_new_listings_uc_sfrcondo_sm_month.csv\n",
+      "processing Metro_mlp_uc_sfr_week.csv\n",
+      "processing Metro_invt_fs_uc_sfrcondo_sm_month.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>RegionID</th>\n",
+       "      <th>SizeRank</th>\n",
+       "      <th>RegionName</th>\n",
+       "      <th>RegionType</th>\n",
+       "      <th>StateName</th>\n",
+       "      <th>Home Type</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Median Listing Price</th>\n",
+       "      <th>Median Listing Price (Smoothed)</th>\n",
+       "      <th>New Listings</th>\n",
+       "      <th>New Listings (Smoothed)</th>\n",
+       "      <th>New Pending (Smoothed)</th>\n",
+       "      <th>New Pending</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2018-01-06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24766.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2018-01-13</td>\n",
+       "      <td>259000.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2018-01-13</td>\n",
+       "      <td>259900.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>71177.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>35229.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2018-01-20</td>\n",
+       "      <td>259900.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2018-01-20</td>\n",
+       "      <td>259900.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72625.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>38281.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2398144</th>\n",
+       "      <td>845172</td>\n",
+       "      <td>769</td>\n",
+       "      <td>Winfield, KS</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2023-12-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>136233.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2398145</th>\n",
+       "      <td>845172</td>\n",
+       "      <td>769</td>\n",
+       "      <td>Winfield, KS</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2024-01-06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>131088.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2398146</th>\n",
+       "      <td>845172</td>\n",
+       "      <td>769</td>\n",
+       "      <td>Winfield, KS</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2024-01-06</td>\n",
+       "      <td>135450.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2398147</th>\n",
+       "      <td>845172</td>\n",
+       "      <td>769</td>\n",
+       "      <td>Winfield, KS</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2024-01-06</td>\n",
+       "      <td>128000.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2398148</th>\n",
+       "      <td>845172</td>\n",
+       "      <td>769</td>\n",
+       "      <td>Winfield, KS</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2024-01-06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>121488.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2398149 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         RegionID  SizeRank     RegionName RegionType StateName  Home Type  \\\n",
+       "0          102001         0  United States    country       NaN  all homes   \n",
+       "1          102001         0  United States    country       NaN        SFR   \n",
+       "2          102001         0  United States    country       NaN  all homes   \n",
+       "3          102001         0  United States    country       NaN        SFR   \n",
+       "4          102001         0  United States    country       NaN  all homes   \n",
+       "...           ...       ...            ...        ...       ...        ...   \n",
+       "2398144    845172       769   Winfield, KS        msa        KS  all homes   \n",
+       "2398145    845172       769   Winfield, KS        msa        KS        SFR   \n",
+       "2398146    845172       769   Winfield, KS        msa        KS        SFR   \n",
+       "2398147    845172       769   Winfield, KS        msa        KS  all homes   \n",
+       "2398148    845172       769   Winfield, KS        msa        KS  all homes   \n",
+       "\n",
+       "               Date  Median Listing Price  Median Listing Price (Smoothed)  \\\n",
+       "0        2018-01-06                   NaN                              NaN   \n",
+       "1        2018-01-13              259000.0                              NaN   \n",
+       "2        2018-01-13              259900.0                              NaN   \n",
+       "3        2018-01-20              259900.0                              NaN   \n",
+       "4        2018-01-20              259900.0                              NaN   \n",
+       "...             ...                   ...                              ...   \n",
+       "2398144  2023-12-31                   NaN                         136233.0   \n",
+       "2398145  2024-01-06                   NaN                         131088.0   \n",
+       "2398146  2024-01-06              135450.0                              NaN   \n",
+       "2398147  2024-01-06              128000.0                              NaN   \n",
+       "2398148  2024-01-06                   NaN                         121488.0   \n",
+       "\n",
+       "         New Listings  New Listings (Smoothed)  New Pending (Smoothed)  \\\n",
+       "0                 NaN                      NaN                     NaN   \n",
+       "1                 NaN                      NaN                     NaN   \n",
+       "2             71177.0                      NaN                     NaN   \n",
+       "3                 NaN                      NaN                     NaN   \n",
+       "4             72625.0                      NaN                     NaN   \n",
+       "...               ...                      ...                     ...   \n",
+       "2398144           NaN                     28.0                     NaN   \n",
+       "2398145           NaN                      NaN                     NaN   \n",
+       "2398146           NaN                      NaN                     NaN   \n",
+       "2398147           NaN                      NaN                     NaN   \n",
+       "2398148           NaN                      NaN                     NaN   \n",
+       "\n",
+       "         New Pending  \n",
+       "0            24766.0  \n",
+       "1                NaN  \n",
+       "2            35229.0  \n",
+       "3                NaN  \n",
+       "4            38281.0  \n",
+       "...              ...  \n",
+       "2398144         24.0  \n",
+       "2398145          NaN  \n",
+       "2398146          NaN  \n",
+       "2398147          NaN  \n",
+       "2398148          NaN  \n",
+       "\n",
+       "[2398149 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
+    "\n",
+    "exclude_columns = [\n",
+    "    \"RegionID\",\n",
+    "    \"SizeRank\",\n",
+    "    \"RegionName\",\n",
+    "    \"RegionType\",\n",
+    "    \"StateName\",\n",
+    "    \"Home Type\",\n",
+    "]\n",
+    "\n",
+    "batches = {\"mlp\": [], \"new_listings\": [], \"new_pending\": []}\n",
+    "\n",
+    "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
+    "    if filename.endswith(\".csv\"):\n",
+    "        print(\"processing \" + filename)\n",
+    "        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
+    "\n",
+    "        # ignore monthly data for now since it is redundant\n",
+    "        if \"monthly\" in filename:\n",
+    "            continue\n",
+    "\n",
+    "        if \"sfrcondo\" in filename:\n",
+    "            cur_df[\"Home Type\"] = \"all homes\"\n",
+    "        elif \"sfr\" in filename:\n",
+    "            cur_df[\"Home Type\"] = \"SFR\"\n",
+    "        elif \"condo\" in filename:\n",
+    "            cur_df[\"Home Type\"] = \"condo/co-op only\"\n",
+    "\n",
+    "        # Identify columns to pivot\n",
+    "        columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
+    "\n",
+    "        smoothed = \"_sm_\" in filename\n",
+    "\n",
+    "        if \"_mlp_\" in filename:\n",
+    "            cur_df = pd.melt(\n",
+    "                cur_df,\n",
+    "                id_vars=exclude_columns,\n",
+    "                value_vars=columns_to_pivot,\n",
+    "                var_name=\"Date\",\n",
+    "                value_name=(\n",
+    "                    \"Median Listing Price\"\n",
+    "                    if not smoothed\n",
+    "                    else \"Median Listing Price (Smoothed)\"\n",
+    "                ),\n",
+    "            )\n",
+    "            batches[\"mlp\"].append(cur_df)\n",
+    "\n",
+    "        elif \"_new_listings_\" in filename:\n",
+    "            cur_df = pd.melt(\n",
+    "                cur_df,\n",
+    "                id_vars=exclude_columns,\n",
+    "                value_vars=columns_to_pivot,\n",
+    "                var_name=\"Date\",\n",
+    "                value_name=(\n",
+    "                    \"New Listings\" if not smoothed else \"New Listings (Smoothed)\"\n",
+    "                ),\n",
+    "            )\n",
+    "            batches[\"new_listings\"].append(cur_df)\n",
+    "\n",
+    "        elif \"new_pending\" in filename:\n",
+    "            cur_df = pd.melt(\n",
+    "                cur_df,\n",
+    "                id_vars=exclude_columns,\n",
+    "                value_vars=columns_to_pivot,\n",
+    "                var_name=\"Date\",\n",
+    "                value_name=\"New Pending\" if not smoothed else \"New Pending (Smoothed)\",\n",
+    "            )\n",
+    "            batches[\"new_pending\"].append(cur_df)\n",
+    "\n",
+    "matching_cols = [\n",
+    "    \"RegionID\",\n",
+    "    \"Date\",\n",
+    "    \"SizeRank\",\n",
+    "    \"RegionName\",\n",
+    "    \"RegionType\",\n",
+    "    \"StateName\",\n",
+    "    \"Home Type\",\n",
+    "]\n",
+    "\n",
+    "combined_batches = [pd.concat(cur_batch) for cur_batch in batches.values()]\n",
+    "\n",
+    "if len(combined_batches) > 0:\n",
+    "    combined_df = combined_batches[0]\n",
+    "    for batch in combined_batches[1:]:\n",
+    "        combined_df = pd.merge(\n",
+    "            combined_df,\n",
+    "            batch,\n",
+    "            on=matching_cols,\n",
+    "            how=\"outer\",\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "combined_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Region ID</th>\n",
+       "      <th>Size Rank</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Region Type</th>\n",
+       "      <th>State</th>\n",
+       "      <th>Home Type</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Median Listing Price</th>\n",
+       "      <th>Median Listing Price (Smoothed)</th>\n",
+       "      <th>New Listings</th>\n",
+       "      <th>New Listings (Smoothed)</th>\n",
+       "      <th>New Pending (Smoothed)</th>\n",
+       "      <th>New Pending</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2018-01-06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24766.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2018-01-13</td>\n",
+       "      <td>259000.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2018-01-13</td>\n",
+       "      <td>259900.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>71177.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>35229.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2018-01-20</td>\n",
+       "      <td>259900.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2018-01-20</td>\n",
+       "      <td>259900.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72625.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>38281.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2398144</th>\n",
+       "      <td>845172</td>\n",
+       "      <td>769</td>\n",
+       "      <td>Winfield, KS</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2023-12-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>136233.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2398145</th>\n",
+       "      <td>845172</td>\n",
+       "      <td>769</td>\n",
+       "      <td>Winfield, KS</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2024-01-06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>131088.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2398146</th>\n",
+       "      <td>845172</td>\n",
+       "      <td>769</td>\n",
+       "      <td>Winfield, KS</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2024-01-06</td>\n",
+       "      <td>135450.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2398147</th>\n",
+       "      <td>845172</td>\n",
+       "      <td>769</td>\n",
+       "      <td>Winfield, KS</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2024-01-06</td>\n",
+       "      <td>128000.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2398148</th>\n",
+       "      <td>845172</td>\n",
+       "      <td>769</td>\n",
+       "      <td>Winfield, KS</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2024-01-06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>121488.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2398149 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Region ID  Size Rank         Region Region Type State  Home Type  \\\n",
+       "0           102001          0  United States     country   NaN  all homes   \n",
+       "1           102001          0  United States     country   NaN        SFR   \n",
+       "2           102001          0  United States     country   NaN  all homes   \n",
+       "3           102001          0  United States     country   NaN        SFR   \n",
+       "4           102001          0  United States     country   NaN  all homes   \n",
+       "...            ...        ...            ...         ...   ...        ...   \n",
+       "2398144     845172        769   Winfield, KS         msa    KS  all homes   \n",
+       "2398145     845172        769   Winfield, KS         msa    KS        SFR   \n",
+       "2398146     845172        769   Winfield, KS         msa    KS        SFR   \n",
+       "2398147     845172        769   Winfield, KS         msa    KS  all homes   \n",
+       "2398148     845172        769   Winfield, KS         msa    KS  all homes   \n",
+       "\n",
+       "               Date  Median Listing Price  Median Listing Price (Smoothed)  \\\n",
+       "0        2018-01-06                   NaN                              NaN   \n",
+       "1        2018-01-13              259000.0                              NaN   \n",
+       "2        2018-01-13              259900.0                              NaN   \n",
+       "3        2018-01-20              259900.0                              NaN   \n",
+       "4        2018-01-20              259900.0                              NaN   \n",
+       "...             ...                   ...                              ...   \n",
+       "2398144  2023-12-31                   NaN                         136233.0   \n",
+       "2398145  2024-01-06                   NaN                         131088.0   \n",
+       "2398146  2024-01-06              135450.0                              NaN   \n",
+       "2398147  2024-01-06              128000.0                              NaN   \n",
+       "2398148  2024-01-06                   NaN                         121488.0   \n",
+       "\n",
+       "         New Listings  New Listings (Smoothed)  New Pending (Smoothed)  \\\n",
+       "0                 NaN                      NaN                     NaN   \n",
+       "1                 NaN                      NaN                     NaN   \n",
+       "2             71177.0                      NaN                     NaN   \n",
+       "3                 NaN                      NaN                     NaN   \n",
+       "4             72625.0                      NaN                     NaN   \n",
+       "...               ...                      ...                     ...   \n",
+       "2398144           NaN                     28.0                     NaN   \n",
+       "2398145           NaN                      NaN                     NaN   \n",
+       "2398146           NaN                      NaN                     NaN   \n",
+       "2398147           NaN                      NaN                     NaN   \n",
+       "2398148           NaN                      NaN                     NaN   \n",
+       "\n",
+       "         New Pending  \n",
+       "0            24766.0  \n",
+       "1                NaN  \n",
+       "2            35229.0  \n",
+       "3                NaN  \n",
+       "4            38281.0  \n",
+       "...              ...  \n",
+       "2398144         24.0  \n",
+       "2398145          NaN  \n",
+       "2398146          NaN  \n",
+       "2398147          NaN  \n",
+       "2398148          NaN  \n",
+       "\n",
+       "[2398149 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_df = combined_df\n",
+    "final_df = final_df.rename(\n",
+    "    columns={\n",
+    "        \"RegionID\": \"Region ID\",\n",
+    "        \"SizeRank\": \"Size Rank\",\n",
+    "        \"RegionName\": \"Region\",\n",
+    "        \"RegionType\": \"Region Type\",\n",
+    "        \"StateName\": \"State\",\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "final_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(FULL_PROCESSED_DIR_PATH):\n",
+    "    os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
+    "\n",
+    "final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

processors/process_new_constructions.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -268,7 +268,7 @@
        "[49487 rows x 10 columns]"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -360,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -588,7 +588,7 @@
        "[49487 rows x 10 columns]"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -610,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [