notebooks/01_Exploratory/2.2-rp-hcad-eda-appraised-value-perc-diff-subdiv-comps-hypothesis-test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load previously cleaned and merged properties (comps)\n",
    "\n",
    "The file being imported below was constructed in the last seven exploratory notebooks (1.0 to 1.6). These data have been cleaned and merged so we can start the exploratory data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:49:54.503060Z",
     "start_time": "2020-12-14T10:49:54.485559Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:49:56.740441Z",
     "start_time": "2020-12-14T10:49:54.505523Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from src.definitions import ROOT_DIR\n",
    "from src.data.utils import Table, save_pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:49:56.773075Z",
     "start_time": "2020-12-14T10:49:56.743638Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.style.use('seaborn-poster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:01.979220Z",
     "start_time": "2020-12-14T10:49:56.774953Z"
    }
   },
   "outputs": [],
   "source": [
    "comps_fn = ROOT_DIR / 'data/interim/2016/comps.pickle'\n",
    "assert comps_fn.exists()\n",
    "\n",
    "with open(comps_fn, 'rb') as f:\n",
    "    comps = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:02.003116Z",
     "start_time": "2020-12-14T10:50:01.980829Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(957686, 83)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comps.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:03.411068Z",
     "start_time": "2020-12-14T10:50:02.006579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 957686 entries, 0 to 957685\n",
      "Data columns (total 83 columns):\n",
      " #   Column                                          Non-Null Count   Dtype         \n",
      "---  ------                                          --------------   -----         \n",
      " 0   acct                                            957686 non-null  uint64        \n",
      " 1   dscr                                            957686 non-null  category      \n",
      " 2   date_erected                                    957686 non-null  uint16        \n",
      " 3   yr_remodel                                      86406 non-null   float64       \n",
      " 4   im_sq_ft                                        957683 non-null  float64       \n",
      " 5   act_ar                                          957686 non-null  uint16        \n",
      " 6   heat_ar                                         957682 non-null  float64       \n",
      " 7   gross_ar                                        957686 non-null  uint16        \n",
      " 8   eff_ar                                          957685 non-null  float64       \n",
      " 9   perimeter                                       956517 non-null  float64       \n",
      " 10  fireplace_masonry_firebrick                     957686 non-null  float32       \n",
      " 11  fireplace_metal_prefab                          957686 non-null  float32       \n",
      " 12  fixtures_addl                                   957686 non-null  float32       \n",
      " 13  fixtures_total                                  957686 non-null  float32       \n",
      " 14  room_bedroom                                    957686 non-null  float32       \n",
      " 15  room_full_bath                                  957686 non-null  float32       \n",
      " 16  room_half_bath                                  957686 non-null  float32       \n",
      " 17  room_rec                                        957686 non-null  float32       \n",
      " 18  room_total                                      957686 non-null  float32       \n",
      " 19  story_height_index                              957686 non-null  float32       \n",
      " 20  zip_code                                        957640 non-null  float64       \n",
      " 21  school_dist                                     957631 non-null  object        \n",
      " 22  neighborhood_code                               957685 non-null  object        \n",
      " 23  market_area_1_dscr                              957686 non-null  object        \n",
      " 24  market_area_2_dscr                              957686 non-null  object        \n",
      " 25  center_code                                     957308 non-null  object        \n",
      " 26  bld_ar                                          957683 non-null  float64       \n",
      " 27  land_ar                                         956654 non-null  float64       \n",
      " 28  acreage                                         957686 non-null  float64       \n",
      " 29  land_val                                        957682 non-null  float64       \n",
      " 30  tot_appr_val                                    957682 non-null  float64       \n",
      " 31  prior_land_val                                  956020 non-null  float64       \n",
      " 32  prior_tot_appr_val                              956020 non-null  float64       \n",
      " 33  new_own_dt                                      957677 non-null  datetime64[ns]\n",
      " 34  lgl_1                                           957686 non-null  object        \n",
      " 35  lgl_2                                           957680 non-null  object        \n",
      " 36  basic_outdoor_kitchen_uts                       429697 non-null  float64       \n",
      " 37  brick_or_stone_detached_garage_uts              429697 non-null  float64       \n",
      " 38  canopy_residential_uts                          429697 non-null  float64       \n",
      " 39  carport_residential_uts                         429697 non-null  float64       \n",
      " 40  cracked_slab_uts                                429697 non-null  float64       \n",
      " 41  custom_outdoor_kitchen_uts                      429697 non-null  float64       \n",
      " 42  foundation_repaired_uts                         429697 non-null  float64       \n",
      " 43  frame_detached_garage_uts                       429697 non-null  float64       \n",
      " 44  frame_detached_garage_w_living_area_abov_uts    429697 non-null  float64       \n",
      " 45  frame_utility_shed_uts                          429697 non-null  float64       \n",
      " 46  gunite_pool_uts                                 429697 non-null  float64       \n",
      " 47  metal_utility_shed_uts                          429697 non-null  float64       \n",
      " 48  pool_spa_with_heater_uts                        429697 non-null  float64       \n",
      " 49  reinforced_concrete_pool_uts                    429697 non-null  float64       \n",
      " 50  residential_other_gross_value_uts               429697 non-null  float64       \n",
      " 51  basic_outdoor_kitchen_grade                     2303 non-null    float64       \n",
      " 52  brick_or_stone_detached_garage_grade            13743 non-null   float64       \n",
      " 53  canopy_residential_grade                        80708 non-null   float64       \n",
      " 54  carport_residential_grade                       73048 non-null   float64       \n",
      " 55  cracked_slab_grade                              16121 non-null   float64       \n",
      " 56  custom_outdoor_kitchen_grade                    4294 non-null    float64       \n",
      " 57  foundation_repaired_grade                       17653 non-null   float64       \n",
      " 58  frame_detached_garage_grade                     180706 non-null  float64       \n",
      " 59  frame_detached_garage_w_living_area_abov_grade  7047 non-null    float64       \n",
      " 60  frame_utility_shed_grade                        87818 non-null   float64       \n",
      " 61  gunite_pool_grade                               87950 non-null   float64       \n",
      " 62  metal_utility_shed_grade                        18365 non-null   float64       \n",
      " 63  pool_spa_with_heater_grade                      37212 non-null   float64       \n",
      " 64  reinforced_concrete_pool_grade                  3729 non-null    float64       \n",
      " 65  residential_other_gross_value_grade             14549 non-null   float64       \n",
      " 66  base_area_pri                                   957683 non-null  float64       \n",
      " 67  base_area_upr                                   957683 non-null  float64       \n",
      " 68  frame_garage_pri                                957683 non-null  float64       \n",
      " 69  mas_brk_garage_pri                              957683 non-null  float64       \n",
      " 70  mas_conc_patio_pri                              957683 non-null  float64       \n",
      " 71  one_story_frame_pri                             957683 non-null  float64       \n",
      " 72  one_story_frame_upr                             957683 non-null  float64       \n",
      " 73  one_story_mas_pri                               957683 non-null  float64       \n",
      " 74  open_frame_porch_pri                            957683 non-null  float64       \n",
      " 75  open_mas_porch_pri                              957683 non-null  float64       \n",
      " 76  cond_desir_util                                 957686 non-null  category      \n",
      " 77  cost_and_design                                 152261 non-null  category      \n",
      " 78  exterior_wall                                   957685 non-null  category      \n",
      " 79  foundation_type                                 957686 non-null  category      \n",
      " 80  grade_adjustment                                957686 non-null  category      \n",
      " 81  heating_ac                                      957685 non-null  category      \n",
      " 82  physical_condition                              957686 non-null  category      \n",
      "dtypes: category(8), datetime64[ns](1), float32(10), float64(53), object(7), uint16(3), uint64(1)\n",
      "memory usage: 509.6+ MB\n"
     ]
    }
   ],
   "source": [
    "comps.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:03.537536Z",
     "start_time": "2020-12-14T10:50:03.413253Z"
    }
   },
   "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>acct</th>\n",
       "      <th>dscr</th>\n",
       "      <th>date_erected</th>\n",
       "      <th>yr_remodel</th>\n",
       "      <th>im_sq_ft</th>\n",
       "      <th>act_ar</th>\n",
       "      <th>heat_ar</th>\n",
       "      <th>gross_ar</th>\n",
       "      <th>eff_ar</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>...</th>\n",
       "      <th>one_story_mas_pri</th>\n",
       "      <th>open_frame_porch_pri</th>\n",
       "      <th>open_mas_porch_pri</th>\n",
       "      <th>cond_desir_util</th>\n",
       "      <th>cost_and_design</th>\n",
       "      <th>exterior_wall</th>\n",
       "      <th>foundation_type</th>\n",
       "      <th>grade_adjustment</th>\n",
       "      <th>heating_ac</th>\n",
       "      <th>physical_condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>118068</th>\n",
       "      <td>691080040006</td>\n",
       "      <td>Low</td>\n",
       "      <td>1948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1945.0</td>\n",
       "      <td>1995</td>\n",
       "      <td>1945.0</td>\n",
       "      <td>1995</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frame / Concrete Blk</td>\n",
       "      <td>Crawl Space</td>\n",
       "      <td>D+</td>\n",
       "      <td>None</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932823</th>\n",
       "      <td>1324570030017</td>\n",
       "      <td>Average</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>2580</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>2580</td>\n",
       "      <td>2194.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frame / Concrete Blk</td>\n",
       "      <td>Slab</td>\n",
       "      <td>C</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316647</th>\n",
       "      <td>973090000002</td>\n",
       "      <td>Average</td>\n",
       "      <td>1979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1524.0</td>\n",
       "      <td>2312</td>\n",
       "      <td>1524.0</td>\n",
       "      <td>2312</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brick / Veneer</td>\n",
       "      <td>Slab</td>\n",
       "      <td>C+</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Fair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27473</th>\n",
       "      <td>300170120045</td>\n",
       "      <td>Low</td>\n",
       "      <td>1940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>1288</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>1288</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frame / Concrete Blk</td>\n",
       "      <td>Crawl Space</td>\n",
       "      <td>D-</td>\n",
       "      <td>None</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88460</th>\n",
       "      <td>621030000002</td>\n",
       "      <td>Average</td>\n",
       "      <td>1926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>1271</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>1271</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brick / Veneer</td>\n",
       "      <td>Crawl Space</td>\n",
       "      <td>C</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833226</th>\n",
       "      <td>1260790020008</td>\n",
       "      <td>Average</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1795.0</td>\n",
       "      <td>2215</td>\n",
       "      <td>1795.0</td>\n",
       "      <td>2215</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frame / Concrete Blk</td>\n",
       "      <td>Slab</td>\n",
       "      <td>C</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946847</th>\n",
       "      <td>1346730020012</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4215.0</td>\n",
       "      <td>5118</td>\n",
       "      <td>4215.0</td>\n",
       "      <td>5118</td>\n",
       "      <td>4383.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brick / Veneer</td>\n",
       "      <td>Slab</td>\n",
       "      <td>A-</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389997</th>\n",
       "      <td>1051930000010</td>\n",
       "      <td>Good</td>\n",
       "      <td>1973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2547.0</td>\n",
       "      <td>2832</td>\n",
       "      <td>2547.0</td>\n",
       "      <td>2832</td>\n",
       "      <td>2523.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brick / Masonry</td>\n",
       "      <td>Slab</td>\n",
       "      <td>B-</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259631</th>\n",
       "      <td>890010000038</td>\n",
       "      <td>Average</td>\n",
       "      <td>1957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>2006</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>2006</td>\n",
       "      <td>1899.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>...</td>\n",
       "      <td>400.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brick / Veneer</td>\n",
       "      <td>Slab</td>\n",
       "      <td>C+</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647202</th>\n",
       "      <td>1171190060016</td>\n",
       "      <td>Good</td>\n",
       "      <td>1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2644.0</td>\n",
       "      <td>3066</td>\n",
       "      <td>2644.0</td>\n",
       "      <td>3066</td>\n",
       "      <td>2586.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>...</td>\n",
       "      <td>156.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frame / Concrete Blk</td>\n",
       "      <td>Slab</td>\n",
       "      <td>B-</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 acct       dscr  date_erected  yr_remodel  im_sq_ft  act_ar  \\\n",
       "118068   691080040006        Low          1948         NaN    1945.0    1995   \n",
       "932823  1324570030017    Average          2012         NaN    2140.0    2580   \n",
       "316647   973090000002    Average          1979         NaN    1524.0    2312   \n",
       "27473    300170120045        Low          1940         NaN    1248.0    1288   \n",
       "88460    621030000002    Average          1926         NaN    1271.0    1271   \n",
       "833226  1260790020008    Average          2007         NaN    1795.0    2215   \n",
       "946847  1346730020012  Excellent          2014         NaN    4215.0    5118   \n",
       "389997  1051930000010       Good          1973         NaN    2547.0    2832   \n",
       "259631   890010000038    Average          1957         NaN    1846.0    2006   \n",
       "647202  1171190060016       Good          1990         NaN    2644.0    3066   \n",
       "\n",
       "        heat_ar  gross_ar  eff_ar  perimeter  ...  one_story_mas_pri  \\\n",
       "118068   1945.0      1995  1962.0      244.0  ...                0.0   \n",
       "932823   2140.0      2580  2194.0      300.0  ...                0.0   \n",
       "316647   1524.0      2312  1750.0      188.0  ...                0.0   \n",
       "27473    1248.0      1288  1261.0      148.0  ...                0.0   \n",
       "88460    1271.0      1271  1271.0      152.0  ...                0.0   \n",
       "833226   1795.0      2215  1954.0      210.0  ...                0.0   \n",
       "946847   4215.0      5118  4383.0      520.0  ...                0.0   \n",
       "389997   2547.0      2832  2523.0      306.0  ...                0.0   \n",
       "259631   1846.0      2006  1899.0      194.0  ...              400.0   \n",
       "647202   2644.0      3066  2586.0      344.0  ...              156.0   \n",
       "\n",
       "        open_frame_porch_pri  open_mas_porch_pri  cond_desir_util  \\\n",
       "118068                  50.0                 0.0          Average   \n",
       "932823                   0.0                40.0          Average   \n",
       "316647                  88.0                 0.0             Fair   \n",
       "27473                   40.0                 0.0          Average   \n",
       "88460                    0.0                 0.0          Average   \n",
       "833226                   0.0                20.0          Average   \n",
       "946847                   0.0               279.0          Average   \n",
       "389997                 150.0               135.0        Very Good   \n",
       "259631                 160.0                 0.0          Average   \n",
       "647202                  96.0                 0.0             Good   \n",
       "\n",
       "        cost_and_design         exterior_wall  foundation_type  \\\n",
       "118068              NaN  Frame / Concrete Blk      Crawl Space   \n",
       "932823              NaN  Frame / Concrete Blk             Slab   \n",
       "316647              NaN        Brick / Veneer             Slab   \n",
       "27473               NaN  Frame / Concrete Blk      Crawl Space   \n",
       "88460               NaN        Brick / Veneer      Crawl Space   \n",
       "833226              NaN  Frame / Concrete Blk             Slab   \n",
       "946847              NaN        Brick / Veneer             Slab   \n",
       "389997              NaN       Brick / Masonry             Slab   \n",
       "259631              NaN        Brick / Veneer             Slab   \n",
       "647202              NaN  Frame / Concrete Blk             Slab   \n",
       "\n",
       "        grade_adjustment       heating_ac  physical_condition  \n",
       "118068                D+             None             Average  \n",
       "932823                 C  Central Heat/AC             Average  \n",
       "316647                C+  Central Heat/AC                Fair  \n",
       "27473                 D-             None             Average  \n",
       "88460                  C  Central Heat/AC                Good  \n",
       "833226                 C  Central Heat/AC             Average  \n",
       "946847                A-  Central Heat/AC             Average  \n",
       "389997                B-  Central Heat/AC                Good  \n",
       "259631                C+  Central Heat/AC             Average  \n",
       "647202                B-  Central Heat/AC             Average  \n",
       "\n",
       "[10 rows x 83 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comps.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop account numbers\n",
    "The account numbers are kept as a reference to the raw data, but should not be used in the EDA. Since they are unique, let's use them as index in the comps dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:03.657232Z",
     "start_time": "2020-12-14T10:50:03.540939Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comps['acct'].is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:03.681176Z",
     "start_time": "2020-12-14T10:50:03.661919Z"
    }
   },
   "outputs": [],
   "source": [
    "comps.set_index('acct', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:03.789867Z",
     "start_time": "2020-12-14T10:50:03.686529Z"
    }
   },
   "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>dscr</th>\n",
       "      <th>date_erected</th>\n",
       "      <th>yr_remodel</th>\n",
       "      <th>im_sq_ft</th>\n",
       "      <th>act_ar</th>\n",
       "      <th>heat_ar</th>\n",
       "      <th>gross_ar</th>\n",
       "      <th>eff_ar</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>fireplace_masonry_firebrick</th>\n",
       "      <th>...</th>\n",
       "      <th>one_story_mas_pri</th>\n",
       "      <th>open_frame_porch_pri</th>\n",
       "      <th>open_mas_porch_pri</th>\n",
       "      <th>cond_desir_util</th>\n",
       "      <th>cost_and_design</th>\n",
       "      <th>exterior_wall</th>\n",
       "      <th>foundation_type</th>\n",
       "      <th>grade_adjustment</th>\n",
       "      <th>heating_ac</th>\n",
       "      <th>physical_condition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>771910210550</th>\n",
       "      <td>Average</td>\n",
       "      <td>1958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>2195</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>2195</td>\n",
       "      <td>1742.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brick / Veneer</td>\n",
       "      <td>Slab</td>\n",
       "      <td>C</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197080050018</th>\n",
       "      <td>Good</td>\n",
       "      <td>1999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3275.0</td>\n",
       "      <td>4369</td>\n",
       "      <td>3275.0</td>\n",
       "      <td>4369</td>\n",
       "      <td>3510.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>863.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frame / Concrete Blk</td>\n",
       "      <td>Slab</td>\n",
       "      <td>B-</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760420320665</th>\n",
       "      <td>Average</td>\n",
       "      <td>1950</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>1456</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>1456</td>\n",
       "      <td>1392.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asbestos</td>\n",
       "      <td>Slab</td>\n",
       "      <td>C-</td>\n",
       "      <td>None</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015490000055</th>\n",
       "      <td>Average</td>\n",
       "      <td>1972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>2160</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>2160</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brick / Veneer</td>\n",
       "      <td>Slab</td>\n",
       "      <td>C+</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720220080099</th>\n",
       "      <td>Average</td>\n",
       "      <td>1945</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>992</td>\n",
       "      <td>952.0</td>\n",
       "      <td>992</td>\n",
       "      <td>965.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>Partial</td>\n",
       "      <td>Brick / Veneer</td>\n",
       "      <td>Crawl Space</td>\n",
       "      <td>C</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1047110000011</th>\n",
       "      <td>Average</td>\n",
       "      <td>1975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>1152</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>1152</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brick / Veneer</td>\n",
       "      <td>Slab</td>\n",
       "      <td>C-</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373120030011</th>\n",
       "      <td>Average</td>\n",
       "      <td>1920</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>1212</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>1212</td>\n",
       "      <td>1136.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>Extensive</td>\n",
       "      <td>Frame / Concrete Blk</td>\n",
       "      <td>Crawl Space</td>\n",
       "      <td>C-</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254000010038</th>\n",
       "      <td>Average</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>1672</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>1672</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frame / Concrete Blk</td>\n",
       "      <td>Slab</td>\n",
       "      <td>C-</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162650150004</th>\n",
       "      <td>Average</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1519.0</td>\n",
       "      <td>2136</td>\n",
       "      <td>1519.0</td>\n",
       "      <td>2136</td>\n",
       "      <td>1687.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>New / Rebuilt</td>\n",
       "      <td>Frame / Concrete Blk</td>\n",
       "      <td>Slab</td>\n",
       "      <td>C</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171590000002</th>\n",
       "      <td>Excellent</td>\n",
       "      <td>1991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4695.0</td>\n",
       "      <td>5399</td>\n",
       "      <td>4695.0</td>\n",
       "      <td>5399</td>\n",
       "      <td>4607.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>New / Rebuilt</td>\n",
       "      <td>Stucco</td>\n",
       "      <td>Slab</td>\n",
       "      <td>A+</td>\n",
       "      <td>Central Heat/AC</td>\n",
       "      <td>Average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    dscr  date_erected  yr_remodel  im_sq_ft  act_ar  heat_ar  \\\n",
       "acct                                                                            \n",
       "771910210550     Average          1958         NaN    1484.0    2195   1484.0   \n",
       "1197080050018       Good          1999         NaN    3275.0    4369   3275.0   \n",
       "760420320665     Average          1950      1978.0    1360.0    1456   1360.0   \n",
       "1015490000055    Average          1972         NaN    1698.0    2160   1698.0   \n",
       "720220080099     Average          1945      1980.0     952.0     992    952.0   \n",
       "1047110000011    Average          1975         NaN    1152.0    1152   1152.0   \n",
       "373120030011     Average          1920      1997.0    1098.0    1212   1098.0   \n",
       "1254000010038    Average          2005         NaN    1540.0    1672   1540.0   \n",
       "162650150004     Average          2007         NaN    1519.0    2136   1519.0   \n",
       "1171590000002  Excellent          1991         NaN    4695.0    5399   4695.0   \n",
       "\n",
       "               gross_ar  eff_ar  perimeter  fireplace_masonry_firebrick  ...  \\\n",
       "acct                                                                     ...   \n",
       "771910210550       2195  1742.0      216.0                          0.0  ...   \n",
       "1197080050018      4369  3510.0      488.0                          0.0  ...   \n",
       "760420320665       1456  1392.0      166.0                          0.0  ...   \n",
       "1015490000055      2160  1872.0      222.0                          1.0  ...   \n",
       "720220080099        992   965.0      140.0                          1.0  ...   \n",
       "1047110000011      1152  1152.0      144.0                          0.0  ...   \n",
       "373120030011       1212  1136.0      158.0                          0.0  ...   \n",
       "1254000010038      1672  1505.0      228.0                          0.0  ...   \n",
       "162650150004       2136  1687.0      160.0                          0.0  ...   \n",
       "1171590000002      5399  4607.0      580.0                          0.0  ...   \n",
       "\n",
       "               one_story_mas_pri  open_frame_porch_pri  open_mas_porch_pri  \\\n",
       "acct                                                                         \n",
       "771910210550                 0.0                 144.0                35.0   \n",
       "1197080050018              863.0                 289.0                45.0   \n",
       "760420320665                 0.0                  96.0                 0.0   \n",
       "1015490000055                0.0                  42.0                 0.0   \n",
       "720220080099                 0.0                   0.0                40.0   \n",
       "1047110000011                0.0                   0.0                 0.0   \n",
       "373120030011                 0.0                 114.0                 0.0   \n",
       "1254000010038                0.0                 132.0                 0.0   \n",
       "162650150004                 0.0                   0.0               248.0   \n",
       "1171590000002                0.0                   0.0                 0.0   \n",
       "\n",
       "               cond_desir_util  cost_and_design         exterior_wall  \\\n",
       "acct                                                                    \n",
       "771910210550              Fair              NaN        Brick / Veneer   \n",
       "1197080050018          Average              NaN  Frame / Concrete Blk   \n",
       "760420320665           Average              NaN              Asbestos   \n",
       "1015490000055          Average              NaN        Brick / Veneer   \n",
       "720220080099           Average          Partial        Brick / Veneer   \n",
       "1047110000011             Fair              NaN        Brick / Veneer   \n",
       "373120030011              Good        Extensive  Frame / Concrete Blk   \n",
       "1254000010038          Average              NaN  Frame / Concrete Blk   \n",
       "162650150004           Average    New / Rebuilt  Frame / Concrete Blk   \n",
       "1171590000002             Fair    New / Rebuilt                Stucco   \n",
       "\n",
       "               foundation_type  grade_adjustment       heating_ac  \\\n",
       "acct                                                                \n",
       "771910210550              Slab                 C  Central Heat/AC   \n",
       "1197080050018             Slab                B-  Central Heat/AC   \n",
       "760420320665              Slab                C-             None   \n",
       "1015490000055             Slab                C+  Central Heat/AC   \n",
       "720220080099       Crawl Space                 C  Central Heat/AC   \n",
       "1047110000011             Slab                C-  Central Heat/AC   \n",
       "373120030011       Crawl Space                C-  Central Heat/AC   \n",
       "1254000010038             Slab                C-  Central Heat/AC   \n",
       "162650150004              Slab                 C  Central Heat/AC   \n",
       "1171590000002             Slab                A+  Central Heat/AC   \n",
       "\n",
       "               physical_condition  \n",
       "acct                               \n",
       "771910210550              Average  \n",
       "1197080050018             Average  \n",
       "760420320665              Average  \n",
       "1015490000055             Average  \n",
       "720220080099                 Good  \n",
       "1047110000011             Average  \n",
       "373120030011                 Good  \n",
       "1254000010038             Average  \n",
       "162650150004              Average  \n",
       "1171590000002             Average  \n",
       "\n",
       "[10 rows x 82 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comps.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build target column\n",
    "The purpose of this work is to find if a given property was fairly appraised when compared with with similar properties.\n",
    "\n",
    "For this we could find the distribution of similar properties total appraised value (`tot_appr_val`) and locate where our target house falls. However, since there is a wide range of property values given their location (neighborhood), physical condition, renovation, and other factors, it might be best to focus on the year-to-year percent change in property value:\n",
    "\n",
    "`yy_pct_change` = (`tot_appr_val` - `prior_tot_appr_val`) / `prior_tot_appr_val`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop NaNs in value colums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:03.820213Z",
     "start_time": "2020-12-14T10:50:03.792173Z"
    }
   },
   "outputs": [],
   "source": [
    "cond0 = comps['prior_tot_appr_val'].isna()\n",
    "cond1 = comps['tot_appr_val'].isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:03.851263Z",
     "start_time": "2020-12-14T10:50:03.822261Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of prior_tot_appr_val missing values: 0.0017396098512456066\n",
      "Percent of tot_appr_val missing values: 4.176734336724146e-06\n"
     ]
    }
   ],
   "source": [
    "print(f'Percent of prior_tot_appr_val missing values: {cond0.sum() / cond0.shape[0]}')\n",
    "print(f'Percent of tot_appr_val missing values: {cond1.sum() / cond1.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:04.394206Z",
     "start_time": "2020-12-14T10:50:03.853874Z"
    }
   },
   "outputs": [],
   "source": [
    "comps = comps.loc[~cond0 & ~cond1, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop samples where prior_tot_appr_val is zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:04.421281Z",
     "start_time": "2020-12-14T10:50:04.396049Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond0 = comps['prior_tot_appr_val'] == 0\n",
    "\n",
    "cond0.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:05.069232Z",
     "start_time": "2020-12-14T10:50:04.423122Z"
    }
   },
   "outputs": [],
   "source": [
    "comps = comps.loc[~cond0, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check tot_appr_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:05.124640Z",
     "start_time": "2020-12-14T10:50:05.071104Z"
    }
   },
   "outputs": [],
   "source": [
    "from src.visualization.visualize import plot_ecdf, plot_counts, plot_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:05.144750Z",
     "start_time": "2020-12-14T10:50:05.126183Z"
    }
   },
   "outputs": [],
   "source": [
    "col ='tot_appr_val'\n",
    "series = comps[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:50:05.219411Z",
     "start_time": "2020-12-14T10:50:05.146631Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9.559880e+05\n",
       "mean     2.191061e+05\n",
       "std      2.800815e+05\n",
       "min      1.000000e+02\n",
       "25%      1.000000e+05\n",
       "50%      1.501055e+05\n",
       "75%      2.342272e+05\n",
       "max      1.726682e+07\n",
       "Name: tot_appr_val, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T10:51:48.184233Z",
     "start_time": "2020-12-14T10:51:47.983453Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(series, col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The high valued properties are masking the distribution of the lower valued properties in this histogram. Let's remake this figure with only properties valued less than $500k."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T11:04:47.737838Z",
     "start_time": "2020-12-14T11:04:47.371660Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(series[series < 500000], col + ': Less than $500k')\n",
    "_ = plt.vlines(292707, 0, 400_000, color='red', label='Target house')\n",
    "_ = plt.legend(loc='upper right')\n",
    "_ = plt.savefig(ROOT_DIR / 'reports/figures/tot_appr_val_hist.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target house is on the high tail of the distribution of all single-family houses in HCAD. Perhaps we can remove the effect of the property's appraisal value magnitude by calculating the year-to-year percent change in value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build year-to-year percent change value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T11:33:10.327602Z",
     "start_time": "2020-12-14T11:33:10.304836Z"
    }
   },
   "outputs": [],
   "source": [
    "comps['yy_pct_change'] = (comps['tot_appr_val'] - comps['prior_tot_appr_val']) / comps['prior_tot_appr_val']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T11:33:10.794709Z",
     "start_time": "2020-12-14T11:33:10.710344Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    955988.000000\n",
       "mean          0.165985\n",
       "std           8.581099\n",
       "min          -0.870447\n",
       "25%           0.030658\n",
       "50%           0.099989\n",
       "75%           0.099999\n",
       "max        7151.941176\n",
       "Name: yy_pct_change, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comps['yy_pct_change'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T11:33:11.421410Z",
     "start_time": "2020-12-14T11:33:11.023323Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(8,8))\n",
    "_ = comps['yy_pct_change'].hist(bins=100)\n",
    "_ = plt.title('Year-to-year perc. change for all comps')\n",
    "_ = plt.xlabel('yy_pct_change')\n",
    "_ = plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T11:33:12.077201Z",
     "start_time": "2020-12-14T11:33:11.959259Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    955988.000000\n",
       "mean          0.165985\n",
       "std           8.581099\n",
       "min          -0.870447\n",
       "25%           0.030658\n",
       "50%           0.099989\n",
       "75%           0.099999\n",
       "max        7151.941176\n",
       "Name: yy_pct_change, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comps['yy_pct_change'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are valid reasons for a property value appreciation from year-to-year to be multiples of the initial value, for example, when a new house is constructed on an existing lot, the previous year value will be the value of the land alone, and the current year value will be the value of the land plus the value of the improvement (built house).\n",
    "\n",
    "Similarly, when the existing house in the prior year is a tear-down, we can expect the value of the property to increase significantly if the owner has removed the old house and built a new one on its place. This is fairly common on neighborhoods with new constructions restrictions, like The Heights.\n",
    "\n",
    "Also, we can expect a large value increase if there has been a major remodeling that either increased the number of rooms or baths (fixtures), common on fixer-uppers, but this work generally increases the value of a property by a fraction, and not whole multipliers.\n",
    "\n",
    "Finally, I think these large (7151%) appreciation values could be errors in the data, or properties that sold under the 10% cap benefits in very expensive neighborhoods. After the sale the cap no longer applies, and thus the property is assessed to its true market value.\n",
    "\n",
    "Let's zoom-in properties appreciated less than 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T11:33:16.281188Z",
     "start_time": "2020-12-14T11:33:14.290919Z"
    }
   },
   "outputs": [],
   "source": [
    "comps_app_lt_1 = comps.loc[comps['yy_pct_change'] < 1, :].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T11:34:35.787284Z",
     "start_time": "2020-12-14T11:34:35.160716Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(8,8))\n",
    "_ = comps_app_lt_1['yy_pct_change'].hist(bins=100, grid=False)\n",
    "_ = plt.vlines(0.093, 5_000, 400_000, color='red', linestyle='--', linewidth=2, label='Target house')\n",
    "_ = plt.title('Comparables appreciated less than 100%')\n",
    "_ = plt.xlabel('yy_pct_change')\n",
    "_ = plt.ylabel('Count')\n",
    "_ = plt.legend(loc='upper right')\n",
    "_ = plt.savefig(ROOT_DIR / 'reports/figures/yy_pct_change_hist.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:16:32.208244Z",
     "start_time": "2020-12-14T12:16:32.051283Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.0833, 0.102]       446146\n",
      "(-0.0102, 0.00854]    157550\n",
      "(0.0646, 0.0833]       63198\n",
      "(0.0459, 0.0646]       59267\n",
      "(0.0272, 0.0459]       51915\n",
      "                       ...  \n",
      "(-0.758, -0.74]            2\n",
      "(-0.683, -0.665]           1\n",
      "(-0.814, -0.796]           1\n",
      "(-0.721, -0.702]           0\n",
      "(-0.852, -0.833]           0\n",
      "Name: yy_pct_change, Length: 100, dtype: int64\n",
      "(0.0833, 0.102]       0.471955\n",
      "(-0.0102, 0.00854]    0.166664\n",
      "(0.0646, 0.0833]      0.066854\n",
      "(0.0459, 0.0646]      0.062696\n",
      "(0.0272, 0.0459]      0.054918\n",
      "                        ...   \n",
      "(-0.758, -0.74]       0.000002\n",
      "(-0.683, -0.665]      0.000001\n",
      "(-0.814, -0.796]      0.000001\n",
      "(-0.721, -0.702]      0.000000\n",
      "(-0.852, -0.833]      0.000000\n",
      "Name: yy_pct_change, Length: 100, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(comps_app_lt_1['yy_pct_change'].value_counts(bins=100))\n",
    "print(comps_app_lt_1['yy_pct_change'].value_counts(bins=100, normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is interesting. There is a spike in the 10% value increase mark. This is possibly related to [Capped Appraisal Values](https://hcad.org/videos/the-10-homestead-cap/), where the appraisal district will cap the value of an existing property if the current year appraised value is more than 10% of last year's. Also, the property has to be homestead exempt for the current and prior year to be eligible for this benefit.\n",
    "\n",
    "We can see in the histogram that about 445000 properties qualified for this capping rule. Neat!\n",
    "\n",
    "There is a lesser spike near 0 % value increase, but its origin is less clear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select the comparable properties\n",
    "\n",
    "The histogram on year-to-tear percent change appraise value above shows that most properties appreciated up to 40%, and depreciated down to 25%. While this is insightful, the spread is too broad as it accounts for almost all properties in the district, with all sorts of conditions, year built, neighborhood, and many other variables, and as a result, the target house is well within this distribution.\n",
    "\n",
    "Next, let's select the comparable properties to my property of interest by counting only properties in the same subdivision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:16:33.927771Z",
     "start_time": "2020-12-14T12:16:33.327476Z"
    }
   },
   "outputs": [],
   "source": [
    "cond0 = comps['lgl_2'].str.contains('PARK AT KLEIN SEC 1', case=False, na=False, regex=False)\n",
    "\n",
    "subdiv = comps.loc[cond0, :].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:16:33.948970Z",
     "start_time": "2020-12-14T12:16:33.930032Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(111, 83)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subdiv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have selected only the houses at the specified subdivision, there are a lot of categorical columns in this subset of samples that are single valued (e.g. `neighborhood_code`, `market_area_1_dscr`, etc.). Let's remove these columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:16:34.590031Z",
     "start_time": "2020-12-14T12:16:34.492374Z"
    }
   },
   "outputs": [],
   "source": [
    "for col in subdiv.columns:\n",
    "    if len(subdiv[col].unique()) == 1:\n",
    "        subdiv.drop(col,inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:16:35.121522Z",
     "start_time": "2020-12-14T12:16:35.091391Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(111, 32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subdiv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop target house from subdivision houses\n",
    "The property of interest is included in the distribution of subdivision houses with the appraised value assigned by HCAD after I protested it. I think that including this sample in the training part of the model could give an unfair advantage and result in optimistic test errors. For this reason, I'll drop it here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:16:36.332354Z",
     "start_time": "2020-12-14T12:16:36.312379Z"
    }
   },
   "outputs": [],
   "source": [
    "target_house = subdiv.loc[1347180010049, :].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:16:36.715140Z",
     "start_time": "2020-12-14T12:16:36.686570Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_erected                             2014\n",
       "im_sq_ft                                 2697\n",
       "act_ar                                   3351\n",
       "heat_ar                                  2697\n",
       "gross_ar                                 3351\n",
       "eff_ar                                   2824\n",
       "perimeter                                 430\n",
       "fireplace_metal_prefab                      1\n",
       "fixtures_addl                               2\n",
       "fixtures_total                             15\n",
       "room_bedroom                                4\n",
       "room_full_bath                              3\n",
       "room_half_bath                              1\n",
       "room_rec                                    2\n",
       "room_total                                  9\n",
       "story_height_index                          2\n",
       "bld_ar                                   2697\n",
       "land_ar                                  6350\n",
       "acreage                                0.1458\n",
       "land_val                                36313\n",
       "tot_appr_val                           263704\n",
       "prior_land_val                          36313\n",
       "prior_tot_appr_val                     267810\n",
       "new_own_dt                2014-07-25 00:00:00\n",
       "lgl_1                             LT 49 BLK 1\n",
       "base_area_pri                            1590\n",
       "base_area_upr                            1107\n",
       "mas_brk_garage_pri                        420\n",
       "open_frame_porch_pri                      120\n",
       "open_mas_porch_pri                        114\n",
       "exterior_wall                  Brick / Veneer\n",
       "yy_pct_change                      -0.0153318\n",
       "Name: 1347180010049, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_house"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:16:37.175031Z",
     "start_time": "2020-12-14T12:16:37.140849Z"
    }
   },
   "outputs": [],
   "source": [
    "subdiv.drop(1347180010049, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot comparables distribution\n",
    "\n",
    "The target house appraised value plotted here is what I received from HCAD (before protesting)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:16:38.371085Z",
     "start_time": "2020-12-14T12:16:37.998566Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(8,8))\n",
    "_ = (subdiv['tot_appr_val']/1000).hist(bins=7, grid=False)\n",
    "_ = plt.title('Houses in the subdivision: 2016 Total Appraised Value')\n",
    "_ = plt.vlines(292707/1000, 0, 35, color='red', label='Target house')\n",
    "_ = plt.xlabel('tot_appr_val x1000 ($)')\n",
    "_ = plt.ylabel('Count')\n",
    "_ = plt.legend(loc='upper right')\n",
    "_ = plt.savefig(ROOT_DIR / 'reports/figures/tot_appr_val_subdiv_hist.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find subdivision comparables\n",
    "In the last figure, we can see that the target house sits on the high end of the distribution of appraised values for the houses in the subdivision. To make a fair comparison, we should find the properties with similar characteristics to the target property. Let's use an unsupervised learning method to find their grouping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:32.852118Z",
     "start_time": "2020-12-14T12:34:32.194182Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:32.912893Z",
     "start_time": "2020-12-14T12:34:32.861572Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "UInt64Index: 110 entries, 1347180010011 to 1347180030030\n",
      "Data columns (total 32 columns):\n",
      " #   Column                  Non-Null Count  Dtype         \n",
      "---  ------                  --------------  -----         \n",
      " 0   date_erected            110 non-null    uint16        \n",
      " 1   im_sq_ft                110 non-null    float64       \n",
      " 2   act_ar                  110 non-null    uint16        \n",
      " 3   heat_ar                 110 non-null    float64       \n",
      " 4   gross_ar                110 non-null    uint16        \n",
      " 5   eff_ar                  110 non-null    float64       \n",
      " 6   perimeter               110 non-null    float64       \n",
      " 7   fireplace_metal_prefab  110 non-null    float32       \n",
      " 8   fixtures_addl           110 non-null    float32       \n",
      " 9   fixtures_total          110 non-null    float32       \n",
      " 10  room_bedroom            110 non-null    float32       \n",
      " 11  room_full_bath          110 non-null    float32       \n",
      " 12  room_half_bath          110 non-null    float32       \n",
      " 13  room_rec                110 non-null    float32       \n",
      " 14  room_total              110 non-null    float32       \n",
      " 15  story_height_index      110 non-null    float32       \n",
      " 16  bld_ar                  110 non-null    float64       \n",
      " 17  land_ar                 110 non-null    float64       \n",
      " 18  acreage                 110 non-null    float64       \n",
      " 19  land_val                110 non-null    float64       \n",
      " 20  tot_appr_val            110 non-null    float64       \n",
      " 21  prior_land_val          110 non-null    float64       \n",
      " 22  prior_tot_appr_val      110 non-null    float64       \n",
      " 23  new_own_dt              110 non-null    datetime64[ns]\n",
      " 24  lgl_1                   110 non-null    object        \n",
      " 25  base_area_pri           110 non-null    float64       \n",
      " 26  base_area_upr           110 non-null    float64       \n",
      " 27  mas_brk_garage_pri      110 non-null    float64       \n",
      " 28  open_frame_porch_pri    110 non-null    float64       \n",
      " 29  open_mas_porch_pri      110 non-null    float64       \n",
      " 30  exterior_wall           110 non-null    category      \n",
      " 31  yy_pct_change           110 non-null    float64       \n",
      "dtypes: category(1), datetime64[ns](1), float32(9), float64(17), object(1), uint16(3)\n",
      "memory usage: 22.2+ KB\n"
     ]
    }
   ],
   "source": [
    "subdiv.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For K-Means to work in theory we should remove the non-numerical features. In addition, I'll remove the features that represent monetary value ($) in such way to build the grouping based only on the physical characteristics (areas, number of rooms, baths, half-baths...) of the properties. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:34.824981Z",
     "start_time": "2020-12-14T12:34:34.788746Z"
    }
   },
   "outputs": [],
   "source": [
    "drop = [\n",
    "    'yy_pct_change',\n",
    "    'land_val',\n",
    "    'tot_appr_val',\n",
    "    'prior_land_val',\n",
    "    'prior_tot_appr_val',\n",
    "    'new_own_dt', # Latest purchase date\n",
    "    'lgl_1', # House lot\n",
    "    'exterior_wall'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:35.919853Z",
     "start_time": "2020-12-14T12:34:35.850284Z"
    }
   },
   "outputs": [],
   "source": [
    "subdiv_numeric = subdiv.drop(drop, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:39.064186Z",
     "start_time": "2020-12-14T12:34:37.885471Z"
    }
   },
   "outputs": [],
   "source": [
    "ks = range(1, 21)\n",
    "inertias = []\n",
    "\n",
    "for k in ks:\n",
    "    # Create a KMeans instance with k clusters: model\n",
    "    model = KMeans(n_clusters=k)\n",
    "    \n",
    "    # Fit model to samples\n",
    "    model.fit(subdiv_numeric)\n",
    "    \n",
    "    # Append the inertia to the list of inertias\n",
    "    inertias.append(model.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:41.826805Z",
     "start_time": "2020-12-14T12:34:41.562156Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ks vs inertias\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.plot(ks, inertias, '-o')\n",
    "plt.xlabel('number of clusters, k')\n",
    "plt.ylabel('inertia')\n",
    "plt.xticks(ks)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like 8 clusters is a good choice. However, after some trial and error I found that k > 3 makes for one cluster with almost all the samples, and a lot of clusters with very few (~3) samples. For this reason I chose k=3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:43.896059Z",
     "start_time": "2020-12-14T12:34:43.875199Z"
    }
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:44.602790Z",
     "start_time": "2020-12-14T12:34:44.516469Z"
    }
   },
   "outputs": [],
   "source": [
    "kmeans.fit(subdiv_numeric)\n",
    "labels = kmeans.predict(subdiv_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:45.509278Z",
     "start_time": "2020-12-14T12:34:45.441788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "labels\n",
       "0         54\n",
       "2         39\n",
       "1         17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'labels': labels}).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TSNE\n",
    "Now let's plot the selected labels using TSNE to find if these groups overlap or are separated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:46.545029Z",
     "start_time": "2020-12-14T12:34:46.523461Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:47.087227Z",
     "start_time": "2020-12-14T12:34:47.056819Z"
    }
   },
   "outputs": [],
   "source": [
    "model = TSNE(learning_rate=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:48.244921Z",
     "start_time": "2020-12-14T12:34:47.618851Z"
    }
   },
   "outputs": [],
   "source": [
    "transformed = model.fit_transform(subdiv_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:34:49.326608Z",
     "start_time": "2020-12-14T12:34:48.812678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "sns.scatterplot(x=transformed[:, 0], y=transformed[:, 1], hue=labels, style=labels, alpha=0.7)\n",
    "_ = plt.savefig(ROOT_DIR / 'reports/figures/TSNE_kmeans.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent! They are well separated indicating that these groups are meaningful.\n",
    "\n",
    "Now let's add the labels found with K-meands back to the features dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:40:42.686791Z",
     "start_time": "2020-12-14T12:40:42.666817Z"
    }
   },
   "outputs": [],
   "source": [
    "subdiv['kmeans_label'] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:40:43.399377Z",
     "start_time": "2020-12-14T12:40:42.956939Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 921.6x633.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x='kmeans_label', y='tot_appr_val', data=subdiv)\n",
    "plt.hlines(292707, 0, 3, colors='red', label='Target House')\n",
    "plt.legend(loc='upper right')\n",
    "_ = plt.savefig(ROOT_DIR / 'reports/figures/tot_appr_val_by_groups.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot the improvement area (`im_sq_ft`) vs. the total appraised value we can see that the k-means groups 0 and 2 represent houses with large area and value, and houses with low area and value, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-14T12:49:21.980833Z",
     "start_time": "2020-12-14T12:49:21.392639Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 921.6x633.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='im_sq_ft', y='tot_appr_val', data=subdiv, hue='kmeans_label')\n",
    "_ = plt.savefig(ROOT_DIR / 'reports/figures/tot_appr_val_vs_im_sq_ft_by_groups.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subdivision comparables\n",
    "Let's predict the target house kmeans cluster, so we can select the appropriate comparables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T21:36:49.363739Z",
     "start_time": "2020-12-15T21:36:49.342666Z"
    }
   },
   "outputs": [],
   "source": [
    "target_house_numeric = target_house.drop(drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T21:36:49.732284Z",
     "start_time": "2020-12-15T21:36:49.701977Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target house label is: 2\n"
     ]
    }
   ],
   "source": [
    "target_house_label = kmeans.predict(target_house_numeric.to_numpy().reshape(1, -1))[0]\n",
    "print(f\"The target house label is: {target_house_label}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T21:36:50.157559Z",
     "start_time": "2020-12-15T21:36:50.119958Z"
    }
   },
   "outputs": [],
   "source": [
    "# Assign label back to target house series\n",
    "target_house['kmeans_label'] = target_house_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T21:36:50.651553Z",
     "start_time": "2020-12-15T21:36:50.628171Z"
    }
   },
   "outputs": [],
   "source": [
    "subdiv_comps = subdiv.loc[subdiv['kmeans_label'] == target_house_label, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T21:36:51.162318Z",
     "start_time": "2020-12-15T21:36:51.126942Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 33)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subdiv_comps.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there are 39 properties comparable to the house of interest. Let's use these houses to build the distribution of comparables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Null-hypothesis test\n",
    "\n",
    "**Population**: Comparable houses appraised by HCAD in the PARK AT KLEIN SEC 1 subdivision.\n",
    "\n",
    "**Significance level**: 5%\n",
    "\n",
    "$H_0$: The 2016 appraised value for the target house was fair relative to its comparables.\n",
    "\n",
    "$H_a$: The 2016 appraised value was unfair, or it doesn't belong to the comparables distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T21:36:56.656737Z",
     "start_time": "2020-12-15T21:36:56.634026Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The population mean is: $258729.44\n",
      "The population standard deviation is: $15373.89\n"
     ]
    }
   ],
   "source": [
    "pop_mean = subdiv_comps['tot_appr_val'].mean()\n",
    "pop_std = subdiv_comps['tot_appr_val'].std(ddof=0)\n",
    "\n",
    "print(f\"The population mean is: ${pop_mean:.2f}\")\n",
    "print(f\"The population standard deviation is: ${pop_std:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T21:37:37.876815Z",
     "start_time": "2020-12-15T21:37:37.847146Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T21:37:38.423643Z",
     "start_time": "2020-12-15T21:37:38.379798Z"
    }
   },
   "outputs": [],
   "source": [
    "zscore = (292707 - pop_mean) / pop_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T21:37:38.827791Z",
     "start_time": "2020-12-15T21:37:38.787700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.210081767245105"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T21:37:39.403376Z",
     "start_time": "2020-12-15T21:37:39.343031Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is: 0.014\n"
     ]
    }
   ],
   "source": [
    "pvalue = 1 - norm.cdf(zscore)\n",
    "print(f\"The p-value is: {pvalue:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value (0.014) is below the significance level (0.05) we can say there is reasonable statistical evidence to reject the the null hypothesis, and thus we have statistical evidence to say that the property appraisal was unfair relative to its comparables.\n",
    "\n",
    "In other words, if the null hypothesis is true, there is a 1.4% probability of getting an appraised value as extreme as $292707 or lager. Since this 1.4\\% is below the 5\\% significance level (z=1.96), we reject the null hypothesis.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-15T21:47:25.563349Z",
     "start_time": "2020-12-15T21:47:25.190306Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(8,8))\n",
    "_ = ((subdiv_comps['tot_appr_val'] - pop_mean)/pop_std).hist(bins=7, grid=False)\n",
    "_ = plt.title('Houses comparables: 2016 Standardized Total Appraised Value')\n",
    "_ = plt.vlines(zscore, 0, 16, color='red', label='Target house')\n",
    "_ = plt.vlines(1.96, 0, 16, color='green', label='5% Significance Level')\n",
    "_ = plt.xlabel('Standardized tot_appr_val')\n",
    "_ = plt.ylabel('Count')\n",
    "_ = plt.legend(loc='upper right')\n",
    "_ = plt.savefig(ROOT_DIR / 'reports/figures/hypothesis_test.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-29T16:15:59.233083Z",
     "start_time": "2020-10-29T16:15:58.742309Z"
    }
   },
   "source": [
    "Now that we have found that there is something wrong with the appraisal value of the target property, let's turn our attention to predicting what would have been a better value, based on the subdivision houses subset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export subdivision data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-24T12:49:17.938358Z",
     "start_time": "2020-11-24T12:49:17.891556Z"
    }
   },
   "outputs": [],
   "source": [
    "save_fn = ROOT_DIR / 'data/interim/2016/subdiv.pickle'\n",
    "save_pickle(subdiv, save_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-24T12:50:51.283484Z",
     "start_time": "2020-11-24T12:50:51.240866Z"
    }
   },
   "outputs": [],
   "source": [
    "save_fn = ROOT_DIR / 'models/kmeans.pickle'\n",
    "save_pickle(kmeans, save_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-24T12:51:27.933534Z",
     "start_time": "2020-11-24T12:51:27.895908Z"
    }
   },
   "outputs": [],
   "source": [
    "save_fn = ROOT_DIR / 'data/processed/target_house.pickle'\n",
    "save_pickle(target_house, save_fn)"
   ]
  }
 ],
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