rental_estimates/rentcast_sales_property_records_estimates.ipynb

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      "provenance": [],
      "authorship_tag": "ABX9TyNHHMaFoxondn+gh2wMNbNM"
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BYRys7lpU-iF"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SM0iggcJnitFe8T7Ay3ymaoijtxF89Pj?usp=sharing)\n",
        "# Rent Cast | Property Records, Estimates, & Market Stats\n",
        "\n",
        "## Overview\n",
        "| Detail Tag            | Information                                                                                        |\n",
        "|-----------------------|----------------------------------------------------------------------------------------------------|\n",
        "| Originally Created By | Ariel Herrera arielherrera@analyticsariel.com |\n",
        "| External References   | API |\n",
        "| Input Datasets        | Source name |\n",
        "| Output Datasets       | Source name |\n",
        "| Input Data Source     | Pandas DataFrame |\n",
        "| Output Data Source    | Pandas DataFrame |\n",
        "\n",
        "## History\n",
        "| Date         | Developed By  | Reason                                                |\n",
        "|--------------|---------------|-------------------------------------------------------|\n",
        "| 1st Sep 2023 | Ariel Herrera | Create notebook. |\n",
        "\n",
        "## Getting Started\n",
        "1. Copy this notebook -> File -> Save a Copy in Drive\n",
        "2. Directions\n",
        "\n",
        "## Useful Resources\n",
        "- [Google Colab Cheat Sheet](https://towardsdatascience.com/cheat-sheet-for-google-colab-63853778c093)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZgrtjtLUi6a"
      },
      "source": [
        "## <font color=\"blue\">Imports</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vWKHxJDUjOJ"
      },
      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "from getpass import getpass\n",
        "from google.colab import files\n",
        "import plotly.express as px # visualization\n",
        "\n",
        "pd.set_option('display.max_columns', None)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9B1YSUk9UsoZ"
      },
      "source": [
        "## <font color=\"blue\">Locals & Constants</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0x9BC1nUqO6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c80ab684-b864-4445-e2a7-3d85edd5cdee"
      },
      "source": [
        "rent_cast_api_key = getpass('Enter the rent cast api key: ')\n",
        "mapbox_api_key = getpass('Enter the mapbox api key: ')\n",
        "px.set_mapbox_access_token(mapbox_api_key)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter the rent cast api key: ··········\n",
            "Enter the mapbox api key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CGCaqKj1UvIo"
      },
      "source": [
        "## <font color=\"blue\">API Endpoints</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1) Property Records 🏠\n",
        "[Api Docs](https://developers.rentcast.io/reference/property-records) | Search for property records in a geographical area, or by a specific address."
      ],
      "metadata": {
        "id": "darW0SzoPH8E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### <font color=\"green\">Single Property Search</font>"
      ],
      "metadata": {
        "id": "f12GaT3MN1TO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# enter property address - streetAddress, city, state, zipCode\n",
        "address = '5430 Brownell St, Orlando, FL 32810'"
      ],
      "metadata": {
        "id": "TuoF6tjqQacP"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get data from rent cast\n",
        "url = \"https://api.rentcast.io/v1/properties\"\n",
        "querystring = {\n",
        "    \"address\": address\n",
        "}\n",
        "headers = {\n",
        "    \"accept\": \"application/json\",\n",
        "    \"X-Api-Key\": rent_cast_api_key # your secret key\n",
        "}\n",
        "response = requests.get(url, headers=headers, params=querystring)\n",
        "print(response.text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5m00H1HXQaU-",
        "outputId": "aea1a0f7-661b-4116-f50f-2ca00a068bab"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{\"addressLine1\":\"5430 Brownell St\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5430 Brownell St, Orlando, FL 32810\",\"assessorID\":\"29-21-29-0356-02-050\",\"bedrooms\":3,\"county\":\"Orange\",\"legalDescription\":\"AVONDALE PARK FIRST ADDITION J/86 LOT 5 BLK B\",\"ownerOccupied\":true,\"squareFootage\":1092,\"subdivision\":\"AVONDALE PARK 1ST ADD\",\"yearBuilt\":1989,\"zoning\":\"R-2\",\"bathrooms\":2,\"lotSize\":6781,\"propertyType\":\"Single Family\",\"lastSalePrice\":161000,\"lastSaleDate\":\"2019-12-26T00:00:00.000Z\",\"features\":{\"cooling\":true,\"coolingType\":\"Central\",\"exteriorType\":\"Concrete Block\",\"floorCount\":1,\"garage\":true,\"garageSpaces\":1,\"garageType\":\"Garage\",\"heating\":true,\"heatingType\":\"Forced Air\",\"roofType\":\"Composition Shingle\",\"unitCount\":1},\"taxAssessments\":{\"2019\":{\"value\":130442,\"land\":40000,\"improvements\":90442},\"2020\":{\"value\":137987,\"land\":40000,\"improvements\":97987},\"2021\":{\"value\":147223,\"land\":40000,\"improvements\":107223},\"2022\":{\"value\":183721,\"land\":50000,\"improvements\":133721}},\"propertyTaxes\":{\"2020\":{\"total\":2445},\"2022\":{\"total\":2914}},\"owner\":{\"names\":[\"HOWARD RUSSELL COHEN\"],\"mailingAddress\":{\"id\":\"1101-Shady-Lane-Dr,-Orlando,-FL-32804\",\"addressLine1\":\"1101 Shady Lane Dr\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32804\"}},\"id\":\"5430-Brownell-St,-Orlando,-FL-32810\",\"longitude\":-81.442822,\"latitude\":28.628838}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view json object\n",
        "response.json()"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "SYoafPKaQaSt",
        "outputId": "ffa0634a-ca1f-4e32-e335-eee2fa665d8f"
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'addressLine1': '5430 Brownell St',\n",
              "  'city': 'Orlando',\n",
              "  'state': 'FL',\n",
              "  'zipCode': '32810',\n",
              "  'formattedAddress': '5430 Brownell St, Orlando, FL 32810',\n",
              "  'assessorID': '29-21-29-0356-02-050',\n",
              "  'bedrooms': 3,\n",
              "  'county': 'Orange',\n",
              "  'legalDescription': 'AVONDALE PARK FIRST ADDITION J/86 LOT 5 BLK B',\n",
              "  'ownerOccupied': True,\n",
              "  'squareFootage': 1092,\n",
              "  'subdivision': 'AVONDALE PARK 1ST ADD',\n",
              "  'yearBuilt': 1989,\n",
              "  'zoning': 'R-2',\n",
              "  'bathrooms': 2,\n",
              "  'lotSize': 6781,\n",
              "  'propertyType': 'Single Family',\n",
              "  'lastSalePrice': 161000,\n",
              "  'lastSaleDate': '2019-12-26T00:00:00.000Z',\n",
              "  'features': {'cooling': True,\n",
              "   'coolingType': 'Central',\n",
              "   'exteriorType': 'Concrete Block',\n",
              "   'floorCount': 1,\n",
              "   'garage': True,\n",
              "   'garageSpaces': 1,\n",
              "   'garageType': 'Garage',\n",
              "   'heating': True,\n",
              "   'heatingType': 'Forced Air',\n",
              "   'roofType': 'Composition Shingle',\n",
              "   'unitCount': 1},\n",
              "  'taxAssessments': {'2019': {'value': 130442,\n",
              "    'land': 40000,\n",
              "    'improvements': 90442},\n",
              "   '2020': {'value': 137987, 'land': 40000, 'improvements': 97987},\n",
              "   '2021': {'value': 147223, 'land': 40000, 'improvements': 107223},\n",
              "   '2022': {'value': 183721, 'land': 50000, 'improvements': 133721}},\n",
              "  'propertyTaxes': {'2020': {'total': 2445}, '2022': {'total': 2914}},\n",
              "  'owner': {'names': ['HOWARD RUSSELL COHEN'],\n",
              "   'mailingAddress': {'id': '1101-Shady-Lane-Dr,-Orlando,-FL-32804',\n",
              "    'addressLine1': '1101 Shady Lane Dr',\n",
              "    'city': 'Orlando',\n",
              "    'state': 'FL',\n",
              "    'zipCode': '32804'}},\n",
              "  'id': '5430-Brownell-St,-Orlando,-FL-32810',\n",
              "  'longitude': -81.442822,\n",
              "  'latitude': 28.628838}]"
            ]
          },
          "metadata": {},
          "execution_count": 5
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        "outputId": "6af22aea-f79f-4c03-ad3b-c2689e8f8772"
      },
      "source": [
        "# view all keys\n",
        "response.json()[0].keys()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['addressLine1', 'city', 'state', 'zipCode', 'formattedAddress', 'assessorID', 'bedrooms', 'county', 'legalDescription', 'ownerOccupied', 'squareFootage', 'subdivision', 'yearBuilt', 'zoning', 'bathrooms', 'lotSize', 'propertyType', 'lastSalePrice', 'lastSaleDate', 'features', 'taxAssessments', 'propertyTaxes', 'owner', 'id', 'longitude', 'latitude'])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# transform into a dataframe with columns and rows\n",
        "df_prop = pd.json_normalize(response.json())\n",
        "df_prop"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       addressLine1     city state zipCode  \\\n",
              "0  5430 Brownell St  Orlando    FL   32810   \n",
              "\n",
              "                      formattedAddress            assessorID  bedrooms  \\\n",
              "0  5430 Brownell St, Orlando, FL 32810  29-21-29-0356-02-050         3   \n",
              "\n",
              "   county                               legalDescription  ownerOccupied  \\\n",
              "0  Orange  AVONDALE PARK FIRST ADDITION J/86 LOT 5 BLK B           True   \n",
              "\n",
              "   squareFootage            subdivision  yearBuilt zoning  bathrooms  lotSize  \\\n",
              "0           1092  AVONDALE PARK 1ST ADD       1989    R-2          2     6781   \n",
              "\n",
              "    propertyType  lastSalePrice              lastSaleDate  \\\n",
              "0  Single Family         161000  2019-12-26T00:00:00.000Z   \n",
              "\n",
              "                                    id  longitude   latitude  \\\n",
              "0  5430-Brownell-St,-Orlando,-FL-32810 -81.442822  28.628838   \n",
              "\n",
              "   features.cooling features.coolingType features.exteriorType  \\\n",
              "0              True              Central        Concrete Block   \n",
              "\n",
              "   features.floorCount  features.garage  features.garageSpaces  \\\n",
              "0                    1             True                      1   \n",
              "\n",
              "  features.garageType  features.heating features.heatingType  \\\n",
              "0              Garage              True           Forced Air   \n",
              "\n",
              "     features.roofType  features.unitCount  taxAssessments.2019.value  \\\n",
              "0  Composition Shingle                   1                     130442   \n",
              "\n",
              "   taxAssessments.2019.land  taxAssessments.2019.improvements  \\\n",
              "0                     40000                             90442   \n",
              "\n",
              "   taxAssessments.2020.value  taxAssessments.2020.land  \\\n",
              "0                     137987                     40000   \n",
              "\n",
              "   taxAssessments.2020.improvements  taxAssessments.2021.value  \\\n",
              "0                             97987                     147223   \n",
              "\n",
              "   taxAssessments.2021.land  taxAssessments.2021.improvements  \\\n",
              "0                     40000                            107223   \n",
              "\n",
              "   taxAssessments.2022.value  taxAssessments.2022.land  \\\n",
              "0                     183721                     50000   \n",
              "\n",
              "   taxAssessments.2022.improvements  propertyTaxes.2020.total  \\\n",
              "0                            133721                      2445   \n",
              "\n",
              "   propertyTaxes.2022.total             owner.names  \\\n",
              "0                      2914  [HOWARD RUSSELL COHEN]   \n",
              "\n",
              "                 owner.mailingAddress.id owner.mailingAddress.addressLine1  \\\n",
              "0  1101-Shady-Lane-Dr,-Orlando,-FL-32804                1101 Shady Lane Dr   \n",
              "\n",
              "  owner.mailingAddress.city owner.mailingAddress.state  \\\n",
              "0                   Orlando                         FL   \n",
              "\n",
              "  owner.mailingAddress.zipCode  \n",
              "0                        32804  "
            ],
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              "      <th></th>\n",
              "      <th>addressLine1</th>\n",
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              "      <th>zipCode</th>\n",
              "      <th>formattedAddress</th>\n",
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              "      <th>id</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>features.cooling</th>\n",
              "      <th>features.coolingType</th>\n",
              "      <th>features.exteriorType</th>\n",
              "      <th>features.floorCount</th>\n",
              "      <th>features.garage</th>\n",
              "      <th>features.garageSpaces</th>\n",
              "      <th>features.garageType</th>\n",
              "      <th>features.heating</th>\n",
              "      <th>features.heatingType</th>\n",
              "      <th>features.roofType</th>\n",
              "      <th>features.unitCount</th>\n",
              "      <th>taxAssessments.2019.value</th>\n",
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              "      <th>taxAssessments.2019.improvements</th>\n",
              "      <th>taxAssessments.2020.value</th>\n",
              "      <th>taxAssessments.2020.land</th>\n",
              "      <th>taxAssessments.2020.improvements</th>\n",
              "      <th>taxAssessments.2021.value</th>\n",
              "      <th>taxAssessments.2021.land</th>\n",
              "      <th>taxAssessments.2021.improvements</th>\n",
              "      <th>taxAssessments.2022.value</th>\n",
              "      <th>taxAssessments.2022.land</th>\n",
              "      <th>taxAssessments.2022.improvements</th>\n",
              "      <th>propertyTaxes.2020.total</th>\n",
              "      <th>propertyTaxes.2022.total</th>\n",
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              "      <th>0</th>\n",
              "      <td>5430 Brownell St</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32810</td>\n",
              "      <td>5430 Brownell St, Orlando, FL 32810</td>\n",
              "      <td>29-21-29-0356-02-050</td>\n",
              "      <td>3</td>\n",
              "      <td>Orange</td>\n",
              "      <td>AVONDALE PARK FIRST ADDITION J/86 LOT 5 BLK B</td>\n",
              "      <td>True</td>\n",
              "      <td>1092</td>\n",
              "      <td>AVONDALE PARK 1ST ADD</td>\n",
              "      <td>1989</td>\n",
              "      <td>R-2</td>\n",
              "      <td>2</td>\n",
              "      <td>6781</td>\n",
              "      <td>Single Family</td>\n",
              "      <td>161000</td>\n",
              "      <td>2019-12-26T00:00:00.000Z</td>\n",
              "      <td>5430-Brownell-St,-Orlando,-FL-32810</td>\n",
              "      <td>-81.442822</td>\n",
              "      <td>28.628838</td>\n",
              "      <td>True</td>\n",
              "      <td>Central</td>\n",
              "      <td>Concrete Block</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>Garage</td>\n",
              "      <td>True</td>\n",
              "      <td>Forced Air</td>\n",
              "      <td>Composition Shingle</td>\n",
              "      <td>1</td>\n",
              "      <td>130442</td>\n",
              "      <td>40000</td>\n",
              "      <td>90442</td>\n",
              "      <td>137987</td>\n",
              "      <td>40000</td>\n",
              "      <td>97987</td>\n",
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              "      <td>40000</td>\n",
              "      <td>107223</td>\n",
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              "      <td>50000</td>\n",
              "      <td>133721</td>\n",
              "      <td>2445</td>\n",
              "      <td>2914</td>\n",
              "      <td>[HOWARD RUSSELL COHEN]</td>\n",
              "      <td>1101-Shady-Lane-Dr,-Orlando,-FL-32804</td>\n",
              "      <td>1101 Shady Lane Dr</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32804</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# [OPTIONAL] export into a csv file\n",
        "file_name = 'ariel_is_my_fav_datascientist.csv' # 'rent_cast_single_prop_output.csv'\n",
        "df_prop.to_csv(file_name, index=False)\n",
        "files.download(file_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "4HUyJdUFRP1R",
        "outputId": "99b6e5f1-1ac7-4039-fb5b-303d6d9306db"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
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              "      if (!google.colab.kernel.accessAllowed) {\n",
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              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
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              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
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              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_277756b3-7cf9-482d-8335-10a07f88b4aa\", \"ariel_is_my_fav_datascientist.csv\", 1553)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### <font color=\"green\">Get List of Properties</font>"
      ],
      "metadata": {
        "id": "00xGfkvOSoYJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_property_records(rent_cast_api_key, address):\n",
        "  # get data from rent cast\n",
        "  url = \"https://api.rentcast.io/v1/properties\"\n",
        "  querystring = {\n",
        "      \"address\": address\n",
        "  }\n",
        "  headers = {\n",
        "      \"accept\": \"application/json\",\n",
        "      \"X-Api-Key\": rent_cast_api_key # your secret key\n",
        "  }\n",
        "  response = requests.get(url, headers=headers, params=querystring)\n",
        "  return response.json()"
      ],
      "metadata": {
        "id": "-pzJhshwSru2"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create list of property addresses\n",
        "property_address_list = [\n",
        "    '4616 Westgrove Way, Orlando, FL 32808',\n",
        "    '3402 Hillmont Cir, Orlando, FL 32817'\n",
        "]\n",
        "property_address_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jt8R1OKZSrsz",
        "outputId": "05550bf7-f7c0-4099-d80b-b64478487a0d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['4616 Westgrove Way, Orlando, FL 32808',\n",
              " '3402 Hillmont Cir, Orlando, FL 32817']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "# get property records for each property address\n",
        "prop_records_resp_list = []\n",
        "\n",
        "# iterate through list\n",
        "print(f'Retrieving data for {len(property_address_list)} records')\n",
        "for prop_address in property_address_list:\n",
        "  print(f'  Getting data for {prop_address}...')\n",
        "  response_json = get_property_records(rent_cast_api_key, prop_address)\n",
        "  prop_records_resp_list.append(response_json[0])\n",
        "print('Loop complete!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JwvXg5STGMS",
        "outputId": "aa27e5e4-0ff1-4fa8-9b89-d462374356cf"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retrieving data for 2 records\n",
            "  Getting data for 4616 Westgrove Way, Orlando, FL 32808...\n",
            "  Getting data for 3402 Hillmont Cir, Orlando, FL 32817...\n",
            "Loop complete!\n",
            "CPU times: user 191 ms, sys: 2.85 ms, total: 194 ms\n",
            "Wall time: 574 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# transform into dataframe\n",
        "df_mulit_prop = pd.json_normalize(prop_records_resp_list)\n",
        "df_mulit_prop"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "id": "zGmjKvyOUVZW",
        "outputId": "d0635a0b-508e-442e-eda6-46d719866256"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         addressLine1     city state zipCode  \\\n",
              "0  4616 Westgrove Way  Orlando    FL   32808   \n",
              "1   3402 Hillmont Cir  Orlando    FL   32817   \n",
              "\n",
              "                        formattedAddress            assessorID  bedrooms  \\\n",
              "0  4616 Westgrove Way, Orlando, FL 32808  06-22-29-1848-00-950         3   \n",
              "1   3402 Hillmont Cir, Orlando, FL 32817  08-22-31-8389-00-470         4   \n",
              "\n",
              "   county           legalDescription ownerOccupied  squareFootage  \\\n",
              "0  Orange  CRYSTAL COVE 36/32 LOT 95          True           1641   \n",
              "1  Orange  SUMMER WOODS 17/70 LOT 47           NaN           1661   \n",
              "\n",
              "    subdivision  yearBuilt     zoning  bathrooms  lotSize   propertyType  \\\n",
              "0  CRYSTAL COVE       1997  R-3A/W/RP          2     5735  Single Family   \n",
              "1  SUMMER WOODS       1987       R-1A          2     7499  Single Family   \n",
              "\n",
              "   lastSalePrice              lastSaleDate  \\\n",
              "0         140000  2016-09-22T00:00:00.000Z   \n",
              "1          79700  1987-06-01T00:00:00.000Z   \n",
              "\n",
              "                                      id  longitude   latitude  \\\n",
              "0  4616-Westgrove-Way,-Orlando,-FL-32808 -81.446604  28.598854   \n",
              "1   3402-Hillmont-Cir,-Orlando,-FL-32817 -81.227434  28.595553   \n",
              "\n",
              "   features.cooling features.coolingType features.exteriorType  \\\n",
              "0              True              Central                Stucco   \n",
              "1              True              Central                Stucco   \n",
              "\n",
              "   features.floorCount  features.garage  features.garageSpaces  \\\n",
              "0                    1             True                    2.0   \n",
              "1                    1             True                    NaN   \n",
              "\n",
              "  features.garageType  features.heating features.heatingType  \\\n",
              "0              Garage              True           Forced Air   \n",
              "1              Garage              True           Forced Air   \n",
              "\n",
              "     features.roofType  features.unitCount  taxAssessments.2020.value  \\\n",
              "0  Composition Shingle                   1                   190412.0   \n",
              "1  Composition Shingle                   1                        NaN   \n",
              "\n",
              "   taxAssessments.2020.land  taxAssessments.2020.improvements  \\\n",
              "0                   34000.0                          156412.0   \n",
              "1                       NaN                               NaN   \n",
              "\n",
              "   taxAssessments.2021.value  taxAssessments.2021.land  \\\n",
              "0                   189118.0                   34000.0   \n",
              "1                        NaN                       NaN   \n",
              "\n",
              "   taxAssessments.2021.improvements  taxAssessments.2022.value  \\\n",
              "0                          155118.0                     194792   \n",
              "1                               NaN                     278853   \n",
              "\n",
              "   propertyTaxes.2020.total  propertyTaxes.2021.total  \\\n",
              "0                    2913.0                    2868.0   \n",
              "1                       NaN                       NaN   \n",
              "\n",
              "   propertyTaxes.2022.total                           owner.names  \\\n",
              "0                      2923                        [CORWIN RUISE]   \n",
              "1                      4480  [RONALD L BLOSSOM, BRENDA G BLOSSOM]   \n",
              "\n",
              "                 owner.mailingAddress.id owner.mailingAddress.addressLine1  \\\n",
              "0  4616-Westgrove-Way,-Orlando,-FL-32808                4616 Westgrove Way   \n",
              "1     14839-Oldham-Dr,-Orlando,-FL-32826                   14839 Oldham Dr   \n",
              "\n",
              "  owner.mailingAddress.city owner.mailingAddress.state  \\\n",
              "0                   Orlando                         FL   \n",
              "1                   Orlando                         FL   \n",
              "\n",
              "  owner.mailingAddress.zipCode  taxAssessments.2022.land  \\\n",
              "0                        32808                       NaN   \n",
              "1                        32826                   55000.0   \n",
              "\n",
              "   taxAssessments.2022.improvements  \n",
              "0                               NaN  \n",
              "1                          223853.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1360004b-ef12-4a14-bfb0-cdb5a7898642\" class=\"colab-df-container\">\n",
              "    <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",
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              "\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>addressLine1</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>zipCode</th>\n",
              "      <th>formattedAddress</th>\n",
              "      <th>assessorID</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>county</th>\n",
              "      <th>legalDescription</th>\n",
              "      <th>ownerOccupied</th>\n",
              "      <th>squareFootage</th>\n",
              "      <th>subdivision</th>\n",
              "      <th>yearBuilt</th>\n",
              "      <th>zoning</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>lotSize</th>\n",
              "      <th>propertyType</th>\n",
              "      <th>lastSalePrice</th>\n",
              "      <th>lastSaleDate</th>\n",
              "      <th>id</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>features.cooling</th>\n",
              "      <th>features.coolingType</th>\n",
              "      <th>features.exteriorType</th>\n",
              "      <th>features.floorCount</th>\n",
              "      <th>features.garage</th>\n",
              "      <th>features.garageSpaces</th>\n",
              "      <th>features.garageType</th>\n",
              "      <th>features.heating</th>\n",
              "      <th>features.heatingType</th>\n",
              "      <th>features.roofType</th>\n",
              "      <th>features.unitCount</th>\n",
              "      <th>taxAssessments.2020.value</th>\n",
              "      <th>taxAssessments.2020.land</th>\n",
              "      <th>taxAssessments.2020.improvements</th>\n",
              "      <th>taxAssessments.2021.value</th>\n",
              "      <th>taxAssessments.2021.land</th>\n",
              "      <th>taxAssessments.2021.improvements</th>\n",
              "      <th>taxAssessments.2022.value</th>\n",
              "      <th>propertyTaxes.2020.total</th>\n",
              "      <th>propertyTaxes.2021.total</th>\n",
              "      <th>propertyTaxes.2022.total</th>\n",
              "      <th>owner.names</th>\n",
              "      <th>owner.mailingAddress.id</th>\n",
              "      <th>owner.mailingAddress.addressLine1</th>\n",
              "      <th>owner.mailingAddress.city</th>\n",
              "      <th>owner.mailingAddress.state</th>\n",
              "      <th>owner.mailingAddress.zipCode</th>\n",
              "      <th>taxAssessments.2022.land</th>\n",
              "      <th>taxAssessments.2022.improvements</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4616 Westgrove Way</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32808</td>\n",
              "      <td>4616 Westgrove Way, Orlando, FL 32808</td>\n",
              "      <td>06-22-29-1848-00-950</td>\n",
              "      <td>3</td>\n",
              "      <td>Orange</td>\n",
              "      <td>CRYSTAL COVE 36/32 LOT 95</td>\n",
              "      <td>True</td>\n",
              "      <td>1641</td>\n",
              "      <td>CRYSTAL COVE</td>\n",
              "      <td>1997</td>\n",
              "      <td>R-3A/W/RP</td>\n",
              "      <td>2</td>\n",
              "      <td>5735</td>\n",
              "      <td>Single Family</td>\n",
              "      <td>140000</td>\n",
              "      <td>2016-09-22T00:00:00.000Z</td>\n",
              "      <td>4616-Westgrove-Way,-Orlando,-FL-32808</td>\n",
              "      <td>-81.446604</td>\n",
              "      <td>28.598854</td>\n",
              "      <td>True</td>\n",
              "      <td>Central</td>\n",
              "      <td>Stucco</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Garage</td>\n",
              "      <td>True</td>\n",
              "      <td>Forced Air</td>\n",
              "      <td>Composition Shingle</td>\n",
              "      <td>1</td>\n",
              "      <td>190412.0</td>\n",
              "      <td>34000.0</td>\n",
              "      <td>156412.0</td>\n",
              "      <td>189118.0</td>\n",
              "      <td>34000.0</td>\n",
              "      <td>155118.0</td>\n",
              "      <td>194792</td>\n",
              "      <td>2913.0</td>\n",
              "      <td>2868.0</td>\n",
              "      <td>2923</td>\n",
              "      <td>[CORWIN RUISE]</td>\n",
              "      <td>4616-Westgrove-Way,-Orlando,-FL-32808</td>\n",
              "      <td>4616 Westgrove Way</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32808</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3402 Hillmont Cir</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32817</td>\n",
              "      <td>3402 Hillmont Cir, Orlando, FL 32817</td>\n",
              "      <td>08-22-31-8389-00-470</td>\n",
              "      <td>4</td>\n",
              "      <td>Orange</td>\n",
              "      <td>SUMMER WOODS 17/70 LOT 47</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1661</td>\n",
              "      <td>SUMMER WOODS</td>\n",
              "      <td>1987</td>\n",
              "      <td>R-1A</td>\n",
              "      <td>2</td>\n",
              "      <td>7499</td>\n",
              "      <td>Single Family</td>\n",
              "      <td>79700</td>\n",
              "      <td>1987-06-01T00:00:00.000Z</td>\n",
              "      <td>3402-Hillmont-Cir,-Orlando,-FL-32817</td>\n",
              "      <td>-81.227434</td>\n",
              "      <td>28.595553</td>\n",
              "      <td>True</td>\n",
              "      <td>Central</td>\n",
              "      <td>Stucco</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Garage</td>\n",
              "      <td>True</td>\n",
              "      <td>Forced Air</td>\n",
              "      <td>Composition Shingle</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>278853</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4480</td>\n",
              "      <td>[RONALD L BLOSSOM, BRENDA G BLOSSOM]</td>\n",
              "      <td>14839-Oldham-Dr,-Orlando,-FL-32826</td>\n",
              "      <td>14839 Oldham Dr</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32826</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>223853.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # [OPTIONAL] export into a csv file\n",
        "# file_name = 'rent_cast_multiple_prop_output.csv'\n",
        "# df_prop.to_csv(file_name, index=False)\n",
        "# files.download(file_name)"
      ],
      "metadata": {
        "id": "8lf5c6ARUy0H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### <font color=\"green\">Get Properties for a Zip Code</font>"
      ],
      "metadata": {
        "id": "0LeRPFnsVBGh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get data from rent cast\n",
        "url = \"https://api.rentcast.io/v1/properties\"\n",
        "querystring = {\n",
        "    \"zipCode\": \"32810\",\n",
        "    \"propertyType\": \"Single Family\",\n",
        "    \"bedrooms\": 3,\n",
        "    \"bathrooms\": 2,\n",
        "    \"limit\": 250\n",
        "}\n",
        "headers = {\n",
        "    \"accept\": \"application/json\",\n",
        "    \"X-Api-Key\": rent_cast_api_key # your secret key\n",
        "}\n",
        "response_zip = requests.get(url, headers=headers, params=querystring)"
      ],
      "metadata": {
        "id": "AHCA_pBFVE7R"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# transform into df\n",
        "df_props_zip = pd.json_normalize(response_zip.json())\n",
        "print(f'Num of records: {len(df_props_zip)}')\n",
        "print(f'Num of columns: {len(df_props_zip.columns)}')\n",
        "df_props_zip.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "7NJylj8HVE5G",
        "outputId": "e491b08e-96ab-496e-d8a1-435b120027f7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of records: 250\n",
            "Num of columns: 65\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             formattedAddress             addressLine1  \\\n",
              "0        3349 Sassafras Ct, Orlando, FL 32810        3349 Sassafras Ct   \n",
              "1          3214 Needles Dr, Orlando, FL 32810          3214 Needles Dr   \n",
              "2          1502 Leeway Ave, Orlando, FL 32810          1502 Leeway Ave   \n",
              "3  4245 Plantation Cove Dr, Orlando, FL 32810  4245 Plantation Cove Dr   \n",
              "4            2924 Drake Dr, Orlando, FL 32810            2924 Drake Dr   \n",
              "\n",
              "      city state zipCode  bathrooms  bedrooms  squareFootage  county  \\\n",
              "0  Orlando    FL   32810          2         3         1718.0  Orange   \n",
              "1  Orlando    FL   32810          2         3         1494.0  Orange   \n",
              "2  Orlando    FL   32810          2         3         1535.0  Orange   \n",
              "3  Orlando    FL   32810          2         3         1184.0  Orange   \n",
              "4  Orlando    FL   32810          2         3         1447.0  Orange   \n",
              "\n",
              "    propertyType            assessorID  \\\n",
              "0  Single Family  28-21-29-7500-00-560   \n",
              "1  Single Family  28-21-29-5430-03-120   \n",
              "2  Single Family  34-21-29-4204-00-310   \n",
              "3  Single Family  32-21-29-5426-00-480   \n",
              "4  Single Family  28-21-29-7460-02-080   \n",
              "\n",
              "                                  legalDescription               subdivision  \\\n",
              "0                     RIVERSIDE WOODS 13/26 LOT 56           RIVERSIDE WOODS   \n",
              "1              MAGNOLIA ESTATES W/134 LOT 12 BLK C             MAGNOLIA ESTS   \n",
              "2       KINGSWOOD MANOR FIFTH ADDITION Z/22 LOT 31   KINGSWOOD MANOR 5TH ADD   \n",
              "3           MAGNOLIA BAY AT MAITLAND 50/73 UNIT 48  MAGNOLIA BAY AT MAITLAND   \n",
              "4  RIVERSIDE ACRES U/137 LOT 8 BLK B SEE 3879/2087           RIVERSIDE ACRES   \n",
              "\n",
              "   yearBuilt zoning  lotSize  lastSalePrice              lastSaleDate  \\\n",
              "0     1984.0    P-D   9314.0       165000.0  2018-04-03T00:00:00.000Z   \n",
              "1     1969.0    R-1  10195.0        75000.0  2014-09-23T00:00:00.000Z   \n",
              "2     1963.0    R-1  10483.0       112500.0  2003-04-10T00:00:00.000Z   \n",
              "3     2004.0    R-3    721.0        45000.0  2012-07-12T00:00:00.000Z   \n",
              "4     1956.0    R-1   8655.0       285000.0  2021-12-22T00:00:00.000Z   \n",
              "\n",
              "                                           id  longitude   latitude  \\\n",
              "0        3349-Sassafras-Ct,-Orlando,-FL-32810 -81.421262  28.630100   \n",
              "1          3214-Needles-Dr,-Orlando,-FL-32810 -81.419975  28.632066   \n",
              "2          1502-Leeway-Ave,-Orlando,-FL-32810 -81.403146  28.614237   \n",
              "3  4245-Plantation-Cove-Dr,-Orlando,-FL-32810 -81.430654  28.625240   \n",
              "4            2924-Drake-Dr,-Orlando,-FL-32810 -81.417206  28.633832   \n",
              "\n",
              "  features.cooling features.coolingType features.exteriorType  \\\n",
              "0             True              Central        Concrete Block   \n",
              "1             True              Central        Concrete Block   \n",
              "2             True              Central        Concrete Block   \n",
              "3             True              Central                Stucco   \n",
              "4             True              Central        Concrete Block   \n",
              "\n",
              "   features.floorCount features.garage  features.garageSpaces  \\\n",
              "0                  1.0            True                    2.0   \n",
              "1                  1.0             NaN                    NaN   \n",
              "2                  1.0            True                    2.0   \n",
              "3                  2.0             NaN                    NaN   \n",
              "4                  1.0            True                    1.0   \n",
              "\n",
              "  features.garageType features.heating features.heatingType  \\\n",
              "0              Garage             True           Forced Air   \n",
              "1                 NaN             True           Forced Air   \n",
              "2              Garage             True           Forced Air   \n",
              "3                 NaN             True           Forced Air   \n",
              "4             Carport             True           Forced Air   \n",
              "\n",
              "     features.roofType  features.unitCount  taxAssessments.2019.value  \\\n",
              "0  Composition Shingle                 1.0                   160403.0   \n",
              "1  Composition Shingle                 1.0                   135587.0   \n",
              "2  Composition Shingle                 1.0                   176372.0   \n",
              "3  Composition Shingle                 1.0                        NaN   \n",
              "4  Composition Shingle                 1.0                   147092.0   \n",
              "\n",
              "   taxAssessments.2019.land  taxAssessments.2019.improvements  \\\n",
              "0                   40000.0                          120403.0   \n",
              "1                   40000.0                           95587.0   \n",
              "2                   42000.0                          134372.0   \n",
              "3                       NaN                               NaN   \n",
              "4                   40000.0                          107092.0   \n",
              "\n",
              "   taxAssessments.2021.value  taxAssessments.2021.land  \\\n",
              "0                   198921.0                   40000.0   \n",
              "1                   161068.0                   40000.0   \n",
              "2                   197700.0                   50000.0   \n",
              "3                        NaN                       NaN   \n",
              "4                   141866.0                   40000.0   \n",
              "\n",
              "   taxAssessments.2021.improvements  propertyTaxes.2020.total  \\\n",
              "0                          158921.0                    3235.0   \n",
              "1                          121068.0                    2498.0   \n",
              "2                          147700.0                    3456.0   \n",
              "3                               NaN                    1697.0   \n",
              "4                          101866.0                    2466.0   \n",
              "\n",
              "   propertyTaxes.2021.total  propertyTaxes.2022.total  \\\n",
              "0                    3470.0                    3905.0   \n",
              "1                    2662.0                    3003.0   \n",
              "2                       NaN                    4082.0   \n",
              "3                       NaN                    2084.0   \n",
              "4                    2532.0                    2343.0   \n",
              "\n",
              "                               owner.names  \\\n",
              "0          [HUNG NGUYEN, THUYNGA THI TRAN]   \n",
              "1                  [IH BORROWER L P 20191]   \n",
              "2                        [REYES DORA ARES]   \n",
              "3                           [JI YUE ZHANG]   \n",
              "4  [ANGELA ELIZABETH KNASE, MATTHEW MOORE]   \n",
              "\n",
              "                    owner.mailingAddress.id owner.mailingAddress.addressLine1  \\\n",
              "0         313-Cortona-Dr,-Orlando,-FL-32828                    313 Cortona Dr   \n",
              "1  1717-Main-St,-STE-2000,-Dallas,-TX-75201                      1717 Main St   \n",
              "2        1502-Leeway-Ave,-Orlando,-FL-32810                   1502 Leeway Ave   \n",
              "3    1682-Barnswallow-Rd,-Yardley,-PA-19067               1682 Barnswallow Rd   \n",
              "4          2924-Drake-Dr,-Orlando,-FL-32810                     2924 Drake Dr   \n",
              "\n",
              "  owner.mailingAddress.city owner.mailingAddress.state  \\\n",
              "0                   Orlando                         FL   \n",
              "1                    Dallas                         TX   \n",
              "2                   Orlando                         FL   \n",
              "3                   Yardley                         PA   \n",
              "4                   Orlando                         FL   \n",
              "\n",
              "  owner.mailingAddress.zipCode features.fireplace  \\\n",
              "0                        32828                NaN   \n",
              "1                        75201               True   \n",
              "2                        32810                NaN   \n",
              "3                        19067                NaN   \n",
              "4                        32810               True   \n",
              "\n",
              "  owner.mailingAddress.addressLine2 ownerOccupied features.pool  \\\n",
              "0                               NaN           NaN           NaN   \n",
              "1                          STE 2000           NaN           NaN   \n",
              "2                               NaN          True          True   \n",
              "3                               NaN           NaN           NaN   \n",
              "4                               NaN          True          True   \n",
              "\n",
              "   taxAssessments.2022.value  taxAssessments.2022.land  \\\n",
              "0                        NaN                       NaN   \n",
              "1                        NaN                       NaN   \n",
              "2                   230641.0                   70000.0   \n",
              "3                   156795.0                   24000.0   \n",
              "4                   171881.0                   50000.0   \n",
              "\n",
              "   taxAssessments.2022.improvements  taxAssessments.2020.value  \\\n",
              "0                               NaN                        NaN   \n",
              "1                               NaN                        NaN   \n",
              "2                          160641.0                        NaN   \n",
              "3                          132795.0                   117935.0   \n",
              "4                          121881.0                        NaN   \n",
              "\n",
              "   taxAssessments.2020.land  taxAssessments.2020.improvements addressLine2  \\\n",
              "0                       NaN                               NaN          NaN   \n",
              "1                       NaN                               NaN          NaN   \n",
              "2                       NaN                               NaN          NaN   \n",
              "3                   20000.0                           97935.0          NaN   \n",
              "4                       NaN                               NaN          NaN   \n",
              "\n",
              "  features.architectureType  propertyTaxes.2018.total  \\\n",
              "0                       NaN                       NaN   \n",
              "1                       NaN                       NaN   \n",
              "2                       NaN                       NaN   \n",
              "3                       NaN                       NaN   \n",
              "4                       NaN                       NaN   \n",
              "\n",
              "   taxAssessments.2018.value  taxAssessments.2018.land  \\\n",
              "0                        NaN                       NaN   \n",
              "1                        NaN                       NaN   \n",
              "2                        NaN                       NaN   \n",
              "3                        NaN                       NaN   \n",
              "4                        NaN                       NaN   \n",
              "\n",
              "   taxAssessments.2018.improvements  propertyTaxes.2019.total  \\\n",
              "0                               NaN                       NaN   \n",
              "1                               NaN                       NaN   \n",
              "2                               NaN                       NaN   \n",
              "3                               NaN                       NaN   \n",
              "4                               NaN                       NaN   \n",
              "\n",
              "  features.poolType  \n",
              "0               NaN  \n",
              "1               NaN  \n",
              "2               NaN  \n",
              "3               NaN  \n",
              "4               NaN  "
            ],
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              "\n",
              "  <div id=\"df-585b1c8c-65b1-43a5-b17a-0189efc2e044\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>formattedAddress</th>\n",
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              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>zipCode</th>\n",
              "      <th>bathrooms</th>\n",
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              "      <th>lastSaleDate</th>\n",
              "      <th>id</th>\n",
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              "      <th>latitude</th>\n",
              "      <th>features.cooling</th>\n",
              "      <th>features.coolingType</th>\n",
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              "      <th>features.heatingType</th>\n",
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              "      <th>taxAssessments.2019.value</th>\n",
              "      <th>taxAssessments.2019.land</th>\n",
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              "      <th>taxAssessments.2021.value</th>\n",
              "      <th>taxAssessments.2021.land</th>\n",
              "      <th>taxAssessments.2021.improvements</th>\n",
              "      <th>propertyTaxes.2020.total</th>\n",
              "      <th>propertyTaxes.2021.total</th>\n",
              "      <th>propertyTaxes.2022.total</th>\n",
              "      <th>owner.names</th>\n",
              "      <th>owner.mailingAddress.id</th>\n",
              "      <th>owner.mailingAddress.addressLine1</th>\n",
              "      <th>owner.mailingAddress.city</th>\n",
              "      <th>owner.mailingAddress.state</th>\n",
              "      <th>owner.mailingAddress.zipCode</th>\n",
              "      <th>features.fireplace</th>\n",
              "      <th>owner.mailingAddress.addressLine2</th>\n",
              "      <th>ownerOccupied</th>\n",
              "      <th>features.pool</th>\n",
              "      <th>taxAssessments.2022.value</th>\n",
              "      <th>taxAssessments.2022.land</th>\n",
              "      <th>taxAssessments.2022.improvements</th>\n",
              "      <th>taxAssessments.2020.value</th>\n",
              "      <th>taxAssessments.2020.land</th>\n",
              "      <th>taxAssessments.2020.improvements</th>\n",
              "      <th>addressLine2</th>\n",
              "      <th>features.architectureType</th>\n",
              "      <th>propertyTaxes.2018.total</th>\n",
              "      <th>taxAssessments.2018.value</th>\n",
              "      <th>taxAssessments.2018.land</th>\n",
              "      <th>taxAssessments.2018.improvements</th>\n",
              "      <th>propertyTaxes.2019.total</th>\n",
              "      <th>features.poolType</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3349 Sassafras Ct, Orlando, FL 32810</td>\n",
              "      <td>3349 Sassafras Ct</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32810</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1718.0</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Single Family</td>\n",
              "      <td>28-21-29-7500-00-560</td>\n",
              "      <td>RIVERSIDE WOODS 13/26 LOT 56</td>\n",
              "      <td>RIVERSIDE WOODS</td>\n",
              "      <td>1984.0</td>\n",
              "      <td>P-D</td>\n",
              "      <td>9314.0</td>\n",
              "      <td>165000.0</td>\n",
              "      <td>2018-04-03T00:00:00.000Z</td>\n",
              "      <td>3349-Sassafras-Ct,-Orlando,-FL-32810</td>\n",
              "      <td>-81.421262</td>\n",
              "      <td>28.630100</td>\n",
              "      <td>True</td>\n",
              "      <td>Central</td>\n",
              "      <td>Concrete Block</td>\n",
              "      <td>1.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Garage</td>\n",
              "      <td>True</td>\n",
              "      <td>Forced Air</td>\n",
              "      <td>Composition Shingle</td>\n",
              "      <td>1.0</td>\n",
              "      <td>160403.0</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>120403.0</td>\n",
              "      <td>198921.0</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>158921.0</td>\n",
              "      <td>3235.0</td>\n",
              "      <td>3470.0</td>\n",
              "      <td>3905.0</td>\n",
              "      <td>[HUNG NGUYEN, THUYNGA THI TRAN]</td>\n",
              "      <td>313-Cortona-Dr,-Orlando,-FL-32828</td>\n",
              "      <td>313 Cortona Dr</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32828</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>1</th>\n",
              "      <td>3214 Needles Dr, Orlando, FL 32810</td>\n",
              "      <td>3214 Needles Dr</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32810</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1494.0</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Single Family</td>\n",
              "      <td>28-21-29-5430-03-120</td>\n",
              "      <td>MAGNOLIA ESTATES W/134 LOT 12 BLK C</td>\n",
              "      <td>MAGNOLIA ESTS</td>\n",
              "      <td>1969.0</td>\n",
              "      <td>R-1</td>\n",
              "      <td>10195.0</td>\n",
              "      <td>75000.0</td>\n",
              "      <td>2014-09-23T00:00:00.000Z</td>\n",
              "      <td>3214-Needles-Dr,-Orlando,-FL-32810</td>\n",
              "      <td>-81.419975</td>\n",
              "      <td>28.632066</td>\n",
              "      <td>True</td>\n",
              "      <td>Central</td>\n",
              "      <td>Concrete Block</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>Forced Air</td>\n",
              "      <td>Composition Shingle</td>\n",
              "      <td>1.0</td>\n",
              "      <td>135587.0</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>95587.0</td>\n",
              "      <td>161068.0</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>121068.0</td>\n",
              "      <td>2498.0</td>\n",
              "      <td>2662.0</td>\n",
              "      <td>3003.0</td>\n",
              "      <td>[IH BORROWER L P 20191]</td>\n",
              "      <td>1717-Main-St,-STE-2000,-Dallas,-TX-75201</td>\n",
              "      <td>1717 Main St</td>\n",
              "      <td>Dallas</td>\n",
              "      <td>TX</td>\n",
              "      <td>75201</td>\n",
              "      <td>True</td>\n",
              "      <td>STE 2000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>1502 Leeway Ave, Orlando, FL 32810</td>\n",
              "      <td>1502 Leeway Ave</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32810</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1535.0</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Single Family</td>\n",
              "      <td>34-21-29-4204-00-310</td>\n",
              "      <td>KINGSWOOD MANOR FIFTH ADDITION Z/22 LOT 31</td>\n",
              "      <td>KINGSWOOD MANOR 5TH ADD</td>\n",
              "      <td>1963.0</td>\n",
              "      <td>R-1</td>\n",
              "      <td>10483.0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>2003-04-10T00:00:00.000Z</td>\n",
              "      <td>1502-Leeway-Ave,-Orlando,-FL-32810</td>\n",
              "      <td>-81.403146</td>\n",
              "      <td>28.614237</td>\n",
              "      <td>True</td>\n",
              "      <td>Central</td>\n",
              "      <td>Concrete Block</td>\n",
              "      <td>1.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Garage</td>\n",
              "      <td>True</td>\n",
              "      <td>Forced Air</td>\n",
              "      <td>Composition Shingle</td>\n",
              "      <td>1.0</td>\n",
              "      <td>176372.0</td>\n",
              "      <td>42000.0</td>\n",
              "      <td>134372.0</td>\n",
              "      <td>197700.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>147700.0</td>\n",
              "      <td>3456.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4082.0</td>\n",
              "      <td>[REYES DORA ARES]</td>\n",
              "      <td>1502-Leeway-Ave,-Orlando,-FL-32810</td>\n",
              "      <td>1502 Leeway Ave</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32810</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>230641.0</td>\n",
              "      <td>70000.0</td>\n",
              "      <td>160641.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>3</th>\n",
              "      <td>4245 Plantation Cove Dr, Orlando, FL 32810</td>\n",
              "      <td>4245 Plantation Cove Dr</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32810</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1184.0</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Single Family</td>\n",
              "      <td>32-21-29-5426-00-480</td>\n",
              "      <td>MAGNOLIA BAY AT MAITLAND 50/73 UNIT 48</td>\n",
              "      <td>MAGNOLIA BAY AT MAITLAND</td>\n",
              "      <td>2004.0</td>\n",
              "      <td>R-3</td>\n",
              "      <td>721.0</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>2012-07-12T00:00:00.000Z</td>\n",
              "      <td>4245-Plantation-Cove-Dr,-Orlando,-FL-32810</td>\n",
              "      <td>-81.430654</td>\n",
              "      <td>28.625240</td>\n",
              "      <td>True</td>\n",
              "      <td>Central</td>\n",
              "      <td>Stucco</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>Forced Air</td>\n",
              "      <td>Composition Shingle</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1697.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2084.0</td>\n",
              "      <td>[JI YUE ZHANG]</td>\n",
              "      <td>1682-Barnswallow-Rd,-Yardley,-PA-19067</td>\n",
              "      <td>1682 Barnswallow Rd</td>\n",
              "      <td>Yardley</td>\n",
              "      <td>PA</td>\n",
              "      <td>19067</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>156795.0</td>\n",
              "      <td>24000.0</td>\n",
              "      <td>132795.0</td>\n",
              "      <td>117935.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>97935.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>2924 Drake Dr, Orlando, FL 32810</td>\n",
              "      <td>2924 Drake Dr</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32810</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1447.0</td>\n",
              "      <td>Orange</td>\n",
              "      <td>Single Family</td>\n",
              "      <td>28-21-29-7460-02-080</td>\n",
              "      <td>RIVERSIDE ACRES U/137 LOT 8 BLK B SEE 3879/2087</td>\n",
              "      <td>RIVERSIDE ACRES</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>R-1</td>\n",
              "      <td>8655.0</td>\n",
              "      <td>285000.0</td>\n",
              "      <td>2021-12-22T00:00:00.000Z</td>\n",
              "      <td>2924-Drake-Dr,-Orlando,-FL-32810</td>\n",
              "      <td>-81.417206</td>\n",
              "      <td>28.633832</td>\n",
              "      <td>True</td>\n",
              "      <td>Central</td>\n",
              "      <td>Concrete Block</td>\n",
              "      <td>1.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Carport</td>\n",
              "      <td>True</td>\n",
              "      <td>Forced Air</td>\n",
              "      <td>Composition Shingle</td>\n",
              "      <td>1.0</td>\n",
              "      <td>147092.0</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>107092.0</td>\n",
              "      <td>141866.0</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>101866.0</td>\n",
              "      <td>2466.0</td>\n",
              "      <td>2532.0</td>\n",
              "      <td>2343.0</td>\n",
              "      <td>[ANGELA ELIZABETH KNASE, MATTHEW MOORE]</td>\n",
              "      <td>2924-Drake-Dr,-Orlando,-FL-32810</td>\n",
              "      <td>2924 Drake Dr</td>\n",
              "      <td>Orlando</td>\n",
              "      <td>FL</td>\n",
              "      <td>32810</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>171881.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>121881.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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          "metadata": {},
          "execution_count": 17
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    {
      "cell_type": "code",
      "source": [
        "# view distribution of properties based on year built\n",
        "fig = px.histogram(df_props_zip, x=\"yearBuilt\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
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          "height": 542
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        "id": "oA9psNYWVE3E",
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      "execution_count": 18,
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    {
      "cell_type": "code",
      "source": [
        "# view distribution of properties based on last sale price\n",
        "fig = px.box(df_props_zip, y=\"lastSalePrice\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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        "id": "8HS7c-TtXh2D",
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      "execution_count": 19,
      "outputs": [
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2) Property Estimates 🏙️\n",
        "[Api Docs](https://developers.rentcast.io/reference/value-estimate) | Returns a property value estimate and comparable properties."
      ],
      "metadata": {
        "id": "IGDJ0eaLXsHA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get sales estimate data from rent cast\n",
        "url = \"https://api.rentcast.io/v1/avm/value\"\n",
        "querystring = {\n",
        "    \"address\":address,\n",
        "}\n",
        "headers = {\n",
        "    \"accept\": \"application/json\",\n",
        "    \"X-Api-Key\": rent_cast_api_key\n",
        "}\n",
        "response_prop_estimate = requests.get(url, headers=headers, params=querystring)\n",
        "response_prop_estimate.text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "L5FmgM7G-DBg",
        "outputId": "b3f9140e-2179-4aa7-e64e-697f3f7a1cef"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'{\"price\":323000,\"priceRangeLow\":226000,\"priceRangeHigh\":420000,\"longitude\":-81.442822,\"latitude\":28.628838,\"comparables\":[{\"id\":\"5430-Brownell-St,-Orlando,-FL-32810\",\"addressLine1\":\"5430 Brownell St\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5430 Brownell St, Orlando, FL 32810\",\"latitude\":28.628838,\"longitude\":-81.442822,\"price\":274900,\"listedDate\":\"2023-06-09T00:00:00.000Z\",\"lastSeenDate\":\"2023-08-23T00:00:00.000Z\",\"daysOld\":1,\"distance\":0,\"correlation\":1,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":3,\"bathrooms\":2,\"squareFootage\":1092,\"lotSize\":6781,\"yearBuilt\":1989},{\"id\":\"8001-Rose-Ave,-Orlando,-FL-32810\",\"addressLine1\":\"8001 Rose Ave\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"8001 Rose Ave, Orlando, FL 32810\",\"latitude\":28.629439,\"longitude\":-81.443367,\"price\":289500,\"listedDate\":\"2023-02-17T00:00:00.000Z\",\"lastSeenDate\":\"2023-04-03T00:00:00.000Z\",\"daysOld\":143,\"distance\":0.0531,\"correlation\":0.9959,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":2,\"bathrooms\":2,\"squareFootage\":1208,\"lotSize\":6499,\"yearBuilt\":1948},{\"id\":\"5429-Vanderlin-St,-Orlando,-FL-32810\",\"addressLine1\":\"5429 Vanderlin St\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5429 Vanderlin St, Orlando, FL 32810\",\"latitude\":28.627584,\"longitude\":-81.442979,\"price\":230000,\"listedDate\":\"2022-06-22T00:00:00.000Z\",\"lastSeenDate\":\"2023-08-23T00:00:00.000Z\",\"daysOld\":1,\"distance\":0.0873,\"correlation\":0.9956,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":2,\"bathrooms\":1,\"squareFootage\":1024,\"lotSize\":6758,\"yearBuilt\":1946},{\"id\":\"7814-Empire-Ave,-Orlando,-FL-32810\",\"addressLine1\":\"7814 Empire Ave\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"7814 Empire Ave, Orlando, FL 32810\",\"latitude\":28.627923,\"longitude\":-81.444931,\"price\":135000,\"listedDate\":\"2023-02-23T00:00:00.000Z\",\"lastSeenDate\":\"2023-06-05T00:00:00.000Z\",\"daysOld\":80,\"distance\":0.1428,\"correlation\":0.9921,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":2,\"bathrooms\":1,\"squareFootage\":740,\"lotSize\":6735,\"yearBuilt\":1952},{\"id\":\"7720-Eastridge-Ct,-Orlando,-FL-32810\",\"addressLine1\":\"7720 Eastridge Ct\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"7720 Eastridge Ct, Orlando, FL 32810\",\"latitude\":28.626627,\"longitude\":-81.441428,\"price\":345000,\"listedDate\":\"2023-06-15T00:00:00.000Z\",\"lastSeenDate\":\"2023-08-23T00:00:00.000Z\",\"daysOld\":1,\"distance\":0.1748,\"correlation\":0.9913,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":3,\"bathrooms\":2,\"squareFootage\":1532,\"lotSize\":9708,\"yearBuilt\":1980},{\"id\":\"7734-Groveridge-Ct,-Orlando,-FL-32810\",\"addressLine1\":\"7734 Groveridge Ct\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"7734 Groveridge Ct, Orlando, FL 32810\",\"latitude\":28.627029,\"longitude\":-81.440552,\"price\":285000,\"listedDate\":\"2023-07-11T00:00:00.000Z\",\"lastSeenDate\":\"2023-08-23T00:00:00.000Z\",\"daysOld\":1,\"distance\":0.1862,\"correlation\":0.9907,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":2,\"bathrooms\":2,\"squareFootage\":988,\"lotSize\":12197,\"yearBuilt\":1981},{\"id\":\"8250-Baywood-Vista-Dr,-Orlando,-FL-32810\",\"addressLine1\":\"8250 Baywood Vista Dr\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"8250 Baywood Vista Dr, Orlando, FL 32810\",\"latitude\":28.630458,\"longitude\":-81.445638,\"price\":470000,\"listedDate\":\"2023-04-30T00:00:00.000Z\",\"lastSeenDate\":\"2023-05-11T00:00:00.000Z\",\"daysOld\":105,\"distance\":0.2044,\"correlation\":0.9887,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":4,\"bathrooms\":2,\"squareFootage\":2200,\"lotSize\":5275,\"yearBuilt\":2003},{\"id\":\"5337-Windridge-Ln,-Orlando,-FL-32810\",\"addressLine1\":\"5337 Windridge Ln\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5337 Windridge Ln, Orlando, FL 32810\",\"latitude\":28.62622,\"longitude\":-81.439718,\"price\":344990,\"listedDate\":\"2023-06-01T00:00:00.000Z\",\"lastSeenDate\":\"2023-08-04T00:00:00.000Z\",\"daysOld\":20,\"distance\":0.2614,\"correlation\":0.9867,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":3,\"bathrooms\":2,\"squareFootage\":1050,\"lotSize\":10454,\"yearBuilt\":1981},{\"id\":\"8489-Baywood-Vista-Dr,-Orlando,-FL-32810\",\"addressLine1\":\"8489 Baywood Vista Dr\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"8489 Baywood Vista Dr, Orlando, FL 32810\",\"latitude\":28.63209,\"longitude\":-81.444665,\"price\":495500,\"listedDate\":\"2023-03-31T00:00:00.000Z\",\"lastSeenDate\":\"2023-05-25T00:00:00.000Z\",\"daysOld\":91,\"distance\":0.2512,\"correlation\":0.9865,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":4,\"bathrooms\":3,\"squareFootage\":2331,\"lotSize\":5779,\"yearBuilt\":2004},{\"id\":\"5603-New-Cambridge-Rd,-Orlando,-FL-32810\",\"addressLine1\":\"5603 New Cambridge Rd\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5603 New Cambridge Rd, Orlando, FL 32810\",\"latitude\":28.631804,\"longitude\":-81.445597,\"price\":365000,\"listedDate\":\"2023-03-22T00:00:00.000Z\",\"lastSeenDate\":\"2023-03-27T00:00:00.000Z\",\"daysOld\":150,\"distance\":0.2655,\"correlation\":0.9853,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":3,\"bathrooms\":2,\"squareFootage\":1292,\"lotSize\":4542,\"yearBuilt\":1998}]}'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view keys\n",
        "response_prop_estimate.json().keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zvorMCC1-C1G",
        "outputId": "219a0ffb-db34-4b0a-bc32-af9e514dd0eb"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['price', 'priceRangeLow', 'priceRangeHigh', 'longitude', 'latitude', 'comparables'])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get sales comparables into a dataframe\n",
        "df_prop_sales_comps = pd.json_normalize(response_prop_estimate.json()['comparables'])\n",
        "print('Estimated price: ${:,}'.format(response_prop_estimate.json()['price']))\n",
        "print(f'Num of records: {len(df_prop_sales_comps)}')\n",
        "print(f'Num of columns: {len(df_prop_sales_comps.columns)}')\n",
        "df_prop_sales_comps.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "EOFHnamJ-7iF",
        "outputId": "169a3c24-c40b-4284-bf72-e4350187514f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estimated price: $323,000\n",
            "Num of records: 10\n",
            "Num of columns: 21\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     id       addressLine1     city state  \\\n",
              "0   5430-Brownell-St,-Orlando,-FL-32810   5430 Brownell St  Orlando    FL   \n",
              "1      8001-Rose-Ave,-Orlando,-FL-32810      8001 Rose Ave  Orlando    FL   \n",
              "2  5429-Vanderlin-St,-Orlando,-FL-32810  5429 Vanderlin St  Orlando    FL   \n",
              "3    7814-Empire-Ave,-Orlando,-FL-32810    7814 Empire Ave  Orlando    FL   \n",
              "4  7720-Eastridge-Ct,-Orlando,-FL-32810  7720 Eastridge Ct  Orlando    FL   \n",
              "\n",
              "  zipCode                      formattedAddress   latitude  longitude   price  \\\n",
              "0   32810   5430 Brownell St, Orlando, FL 32810  28.628838 -81.442822  274900   \n",
              "1   32810      8001 Rose Ave, Orlando, FL 32810  28.629439 -81.443367  289500   \n",
              "2   32810  5429 Vanderlin St, Orlando, FL 32810  28.627584 -81.442979  230000   \n",
              "3   32810    7814 Empire Ave, Orlando, FL 32810  28.627923 -81.444931  135000   \n",
              "4   32810  7720 Eastridge Ct, Orlando, FL 32810  28.626627 -81.441428  345000   \n",
              "\n",
              "                 listedDate              lastSeenDate  daysOld  distance  \\\n",
              "0  2023-06-09T00:00:00.000Z  2023-08-23T00:00:00.000Z        1    0.0000   \n",
              "1  2023-02-17T00:00:00.000Z  2023-04-03T00:00:00.000Z      143    0.0531   \n",
              "2  2022-06-22T00:00:00.000Z  2023-08-23T00:00:00.000Z        1    0.0873   \n",
              "3  2023-02-23T00:00:00.000Z  2023-06-05T00:00:00.000Z       80    0.1428   \n",
              "4  2023-06-15T00:00:00.000Z  2023-08-23T00:00:00.000Z        1    0.1748   \n",
              "\n",
              "   correlation  county   propertyType  bedrooms  bathrooms  squareFootage  \\\n",
              "0       1.0000  Orange  Single Family         3          2           1092   \n",
              "1       0.9959  Orange  Single Family         2          2           1208   \n",
              "2       0.9956  Orange  Single Family         2          1           1024   \n",
              "3       0.9921  Orange  Single Family         2          1            740   \n",
              "4       0.9913  Orange  Single Family         3          2           1532   \n",
              "\n",
              "   lotSize  yearBuilt  \n",
              "0     6781       1989  \n",
              "1     6499       1948  \n",
              "2     6758       1946  \n",
              "3     6735       1952  \n",
              "4     9708       1980  "
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              "      <td>28.627584</td>\n",
              "      <td>-81.442979</td>\n",
              "      <td>230000</td>\n",
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              "      <td>2023-08-23T00:00:00.000Z</td>\n",
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          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get subject property attributes into a dataframe\n",
        "comp_cols = ['price', 'latitude', 'longitude']\n",
        "d_sales_comps = {}\n",
        "for c in comp_cols:\n",
        "  d_sales_comps[c] = [response_prop_estimate.json()[c]]\n",
        "df_sales_comp_subj = pd.DataFrame(data=d_sales_comps)\n",
        "df_sales_comp_subj['type'] = 'subject property'\n",
        "df_sales_comp_subj"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "ASxy6laTCKas",
        "outputId": "a34f0d94-f50f-4526-fb41-7a15e1e5f54f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    price   latitude  longitude              type\n",
              "0  323000  28.628838 -81.442822  subject property"
            ],
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              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c6c7017a-4303-4fbc-8f14-8a61fae14557');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get saels comp attributes into a dataframe\n",
        "df_sales_comp = df_prop_sales_comps[['price', 'latitude', 'longitude']]\n",
        "df_sales_comp['type'] = 'comparables'\n",
        "df_sales_comp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "bkMwN9pmCv68",
        "outputId": "92feabae-da13-41d0-fd83-dbbfd8b508b1"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-8e90c2ce9232>:3: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    price   latitude  longitude         type\n",
              "0  274900  28.628838 -81.442822  comparables\n",
              "1  289500  28.629439 -81.443367  comparables\n",
              "2  230000  28.627584 -81.442979  comparables\n",
              "3  135000  28.627923 -81.444931  comparables\n",
              "4  345000  28.626627 -81.441428  comparables\n",
              "5  285000  28.627029 -81.440552  comparables\n",
              "6  470000  28.630458 -81.445638  comparables\n",
              "7  344990  28.626220 -81.439718  comparables\n",
              "8  495500  28.632090 -81.444665  comparables\n",
              "9  365000  28.631804 -81.445597  comparables"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>type</th>\n",
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              "      <td>28.628838</td>\n",
              "      <td>-81.442822</td>\n",
              "      <td>comparables</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>289500</td>\n",
              "      <td>28.629439</td>\n",
              "      <td>-81.443367</td>\n",
              "      <td>comparables</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>230000</td>\n",
              "      <td>28.627584</td>\n",
              "      <td>-81.442979</td>\n",
              "      <td>comparables</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>135000</td>\n",
              "      <td>28.627923</td>\n",
              "      <td>-81.444931</td>\n",
              "      <td>comparables</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>345000</td>\n",
              "      <td>28.626627</td>\n",
              "      <td>-81.441428</td>\n",
              "      <td>comparables</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>285000</td>\n",
              "      <td>28.627029</td>\n",
              "      <td>-81.440552</td>\n",
              "      <td>comparables</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>470000</td>\n",
              "      <td>28.630458</td>\n",
              "      <td>-81.445638</td>\n",
              "      <td>comparables</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>344990</td>\n",
              "      <td>28.626220</td>\n",
              "      <td>-81.439718</td>\n",
              "      <td>comparables</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>495500</td>\n",
              "      <td>28.632090</td>\n",
              "      <td>-81.444665</td>\n",
              "      <td>comparables</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>365000</td>\n",
              "      <td>28.631804</td>\n",
              "      <td>-81.445597</td>\n",
              "      <td>comparables</td>\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
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              "      let quickchartButtonEl =\n",
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            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# concatenate subj & comps dataframe\n",
        "df_sales_comps_fig = pd.concat([df_sales_comp_subj, df_sales_comp])\n",
        "# plot coordinates in map\n",
        "fig = px.scatter_mapbox(df_sales_comps_fig, lat=\"latitude\", lon=\"longitude\", size=\"price\", color=\"type\", zoom=15, title=f\"<b>{address}</b><br>Property Sales Comparables\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "APw0C4-QAR3z",
        "outputId": "4d27b16c-085f-468e-9284-1d1f69c899d2"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "markdown",
      "source": [
        "### 3) Rent Estimates 🤑\n",
        "[Api Docs](https://developers.rentcast.io/reference/rent-estimate-long-term) | Returns a property rent estimate and comparable properties."
      ],
      "metadata": {
        "id": "i7rD-wS_X4LZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get rental estimate data from rent cast\n",
        "url = \"https://api.rentcast.io/v1/avm/rent/long-term\"\n",
        "querystring = {\n",
        "    \"address\":address,\n",
        "}\n",
        "headers = {\n",
        "    \"accept\": \"application/json\",\n",
        "    \"X-Api-Key\": rent_cast_api_key\n",
        "}\n",
        "response_prop_rent_estimate = requests.get(url, headers=headers, params=querystring)\n",
        "response_prop_rent_estimate.text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "DDjvI6c2D5g0",
        "outputId": "241fb60c-b4dd-4469-9ad7-cf9b84705db3"
      },
      "execution_count": 28,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'{\"rent\":2090,\"rentRangeLow\":1810,\"rentRangeHigh\":2370,\"longitude\":-81.442822,\"latitude\":28.628838,\"comparables\":[{\"id\":\"5458-Windridge-Ln,-Lockhart,-FL-32810\",\"addressLine1\":\"5458 Windridge Ln\",\"city\":\"Lockhart\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5458 Windridge Ln, Lockhart, FL 32810\",\"latitude\":28.626034,\"longitude\":-81.443057,\"price\":1695,\"listedDate\":\"2023-03-17T00:00:00.000Z\",\"lastSeenDate\":\"2023-05-03T00:00:00.000Z\",\"daysOld\":113,\"distance\":0.1945,\"correlation\":0.9893,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":4,\"squareFootage\":1108},{\"id\":\"5458-Windridge-Ln,-Orlando,-FL-32810\",\"addressLine1\":\"5458 Windridge Ln\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5458 Windridge Ln, Orlando, FL 32810\",\"latitude\":28.626034,\"longitude\":-81.443057,\"price\":1695,\"listedDate\":\"2023-03-16T00:00:00.000Z\",\"lastSeenDate\":\"2023-05-01T00:00:00.000Z\",\"daysOld\":115,\"distance\":0.1945,\"correlation\":0.9893,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":4,\"bathrooms\":2,\"squareFootage\":1108,\"lotSize\":8212,\"yearBuilt\":1980},{\"id\":\"5541-New-Cambridge-Rd,-Orlando,-FL-32810\",\"addressLine1\":\"5541 New Cambridge Rd\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5541 New Cambridge Rd, Orlando, FL 32810\",\"latitude\":28.631799,\"longitude\":-81.445176,\"price\":2235,\"listedDate\":\"2023-07-16T00:00:00.000Z\",\"lastSeenDate\":\"2023-08-22T00:00:00.000Z\",\"daysOld\":2,\"distance\":0.2497,\"correlation\":0.9877,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":3,\"bathrooms\":2,\"squareFootage\":1292},{\"id\":\"8507-Baywood-Vista-Dr,-Orlando,-FL-32810\",\"addressLine1\":\"8507 Baywood Vista Dr\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"8507 Baywood Vista Dr, Orlando, FL 32810\",\"latitude\":28.632086,\"longitude\":-81.444345,\"price\":2495,\"listedDate\":\"2023-07-07T00:00:00.000Z\",\"lastSeenDate\":\"2023-07-16T00:00:00.000Z\",\"daysOld\":39,\"distance\":0.2429,\"correlation\":0.9876,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":3,\"bathrooms\":2,\"squareFootage\":2044,\"lotSize\":6352,\"yearBuilt\":2004},{\"id\":\"5541-New-Cambridge-Rd,-Lockhart,-FL-32810\",\"addressLine1\":\"5541 New Cambridge Rd\",\"city\":\"Lockhart\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5541 New Cambridge Rd, Lockhart, FL 32810\",\"latitude\":28.631799,\"longitude\":-81.445176,\"price\":2550,\"listedDate\":\"2023-05-16T00:00:00.000Z\",\"lastSeenDate\":\"2023-07-06T00:00:00.000Z\",\"daysOld\":49,\"distance\":0.2497,\"correlation\":0.9872,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":3,\"bathrooms\":2,\"squareFootage\":1292},{\"id\":\"5550-New-Cambridge-Rd,-Lockhart,-FL-32810\",\"addressLine1\":\"5550 New Cambridge Rd\",\"city\":\"Lockhart\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5550 New Cambridge Rd, Lockhart, FL 32810\",\"latitude\":28.631388,\"longitude\":-81.445411,\"price\":1875,\"listedDate\":\"2023-03-02T00:00:00.000Z\",\"lastSeenDate\":\"2023-03-23T00:00:00.000Z\",\"daysOld\":154,\"distance\":0.2363,\"correlation\":0.9868,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":3,\"bathrooms\":2,\"squareFootage\":1292},{\"id\":\"5550-New-Cambridge-Rd,-Orlando,-FL-32810\",\"addressLine1\":\"5550 New Cambridge Rd\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5550 New Cambridge Rd, Orlando, FL 32810\",\"latitude\":28.631388,\"longitude\":-81.445411,\"price\":1875,\"listedDate\":\"2022-11-10T20:06:33.000Z\",\"lastSeenDate\":\"2023-03-22T00:00:00.000Z\",\"daysOld\":155,\"distance\":0.2363,\"correlation\":0.9868,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":3,\"bathrooms\":2,\"squareFootage\":1292,\"lotSize\":4499,\"yearBuilt\":1998},{\"id\":\"5337R-Windridge-Ln,-Orlando,-FL-32810\",\"addressLine1\":\"5337R Windridge Ln\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5337R Windridge Ln, Orlando, FL 32810\",\"latitude\":28.626165,\"longitude\":-81.439713,\"price\":2200,\"listedDate\":\"2023-03-31T00:00:00.000Z\",\"lastSeenDate\":\"2023-04-02T00:00:00.000Z\",\"daysOld\":144,\"distance\":0.2642,\"correlation\":0.9855,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":3,\"bathrooms\":2,\"squareFootage\":1050},{\"id\":\"5655-New-Cambridge-Rd,-Orlando,-FL-32810\",\"addressLine1\":\"5655 New Cambridge Rd\",\"city\":\"Orlando\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5655 New Cambridge Rd, Orlando, FL 32810\",\"latitude\":28.631118,\"longitude\":-81.446695,\"price\":2295,\"listedDate\":\"2023-04-26T00:00:00.000Z\",\"lastSeenDate\":\"2023-06-20T00:00:00.000Z\",\"daysOld\":65,\"distance\":0.2831,\"correlation\":0.9854,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":3,\"bathrooms\":2,\"squareFootage\":1292},{\"id\":\"5418-Ashmeade-Rd,-Lockhart,-FL-32810\",\"addressLine1\":\"5418 Ashmeade Rd\",\"city\":\"Lockhart\",\"state\":\"FL\",\"zipCode\":\"32810\",\"formattedAddress\":\"5418 Ashmeade Rd, Lockhart, FL 32810\",\"latitude\":28.633249,\"longitude\":-81.443056,\"price\":1960,\"listedDate\":\"2023-08-04T00:00:00.000Z\",\"lastSeenDate\":\"2023-08-05T00:00:00.000Z\",\"daysOld\":19,\"distance\":0.3054,\"correlation\":0.9848,\"county\":\"Orange\",\"propertyType\":\"Single Family\",\"bedrooms\":3,\"bathrooms\":2,\"squareFootage\":1264}]}'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view keys\n",
        "response_prop_rent_estimate.json().keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlJRrGG7D5eS",
        "outputId": "0d65eef7-6985-4600-961a-63e050205ade"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['rent', 'rentRangeLow', 'rentRangeHigh', 'longitude', 'latitude', 'comparables'])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get rental comparables into a dataframe\n",
        "df_prop_rent_comps = pd.json_normalize(response_prop_rent_estimate.json()['comparables'])\n",
        "print('Estimated rent: ${:,}'.format(response_prop_rent_estimate.json()['rent']))\n",
        "print(f'Num of records: {len(df_prop_rent_comps)}')\n",
        "print(f'Num of columns: {len(df_prop_rent_comps.columns)}')\n",
        "df_prop_rent_comps.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "aaAbHC3uD5ak",
        "outputId": "27690629-0c23-4f67-88e3-8ed582af962e"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estimated rent: $2,090\n",
            "Num of records: 10\n",
            "Num of columns: 21\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          id           addressLine1      city  \\\n",
              "0      5458-Windridge-Ln,-Lockhart,-FL-32810      5458 Windridge Ln  Lockhart   \n",
              "1       5458-Windridge-Ln,-Orlando,-FL-32810      5458 Windridge Ln   Orlando   \n",
              "2   5541-New-Cambridge-Rd,-Orlando,-FL-32810  5541 New Cambridge Rd   Orlando   \n",
              "3   8507-Baywood-Vista-Dr,-Orlando,-FL-32810  8507 Baywood Vista Dr   Orlando   \n",
              "4  5541-New-Cambridge-Rd,-Lockhart,-FL-32810  5541 New Cambridge Rd  Lockhart   \n",
              "\n",
              "  state zipCode                           formattedAddress   latitude  \\\n",
              "0    FL   32810      5458 Windridge Ln, Lockhart, FL 32810  28.626034   \n",
              "1    FL   32810       5458 Windridge Ln, Orlando, FL 32810  28.626034   \n",
              "2    FL   32810   5541 New Cambridge Rd, Orlando, FL 32810  28.631799   \n",
              "3    FL   32810   8507 Baywood Vista Dr, Orlando, FL 32810  28.632086   \n",
              "4    FL   32810  5541 New Cambridge Rd, Lockhart, FL 32810  28.631799   \n",
              "\n",
              "   longitude  price                listedDate              lastSeenDate  \\\n",
              "0 -81.443057   1695  2023-03-17T00:00:00.000Z  2023-05-03T00:00:00.000Z   \n",
              "1 -81.443057   1695  2023-03-16T00:00:00.000Z  2023-05-01T00:00:00.000Z   \n",
              "2 -81.445176   2235  2023-07-16T00:00:00.000Z  2023-08-22T00:00:00.000Z   \n",
              "3 -81.444345   2495  2023-07-07T00:00:00.000Z  2023-07-16T00:00:00.000Z   \n",
              "4 -81.445176   2550  2023-05-16T00:00:00.000Z  2023-07-06T00:00:00.000Z   \n",
              "\n",
              "   daysOld  distance  correlation  county   propertyType  bedrooms  \\\n",
              "0      113    0.1945       0.9893  Orange  Single Family         4   \n",
              "1      115    0.1945       0.9893  Orange  Single Family         4   \n",
              "2        2    0.2497       0.9877  Orange  Single Family         3   \n",
              "3       39    0.2429       0.9876  Orange  Single Family         3   \n",
              "4       49    0.2497       0.9872  Orange  Single Family         3   \n",
              "\n",
              "   squareFootage  bathrooms  lotSize  yearBuilt  \n",
              "0           1108        NaN      NaN        NaN  \n",
              "1           1108        2.0   8212.0     1980.0  \n",
              "2           1292        2.0      NaN        NaN  \n",
              "3           2044        2.0   6352.0     2004.0  \n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get subject property attributes into a dataframe\n",
        "rent_comp_cols = ['rent', 'latitude', 'longitude']\n",
        "d_rent_comps = {}\n",
        "for c in rent_comp_cols:\n",
        "  d_rent_comps[c] = [response_prop_rent_estimate.json()[c]]\n",
        "df_rent_comp_subj = pd.DataFrame(data=d_rent_comps).rename(columns={'rent': 'price'})\n",
        "df_rent_comp_subj['type'] = 'subject property'\n",
        "df_rent_comp_subj"
      ],
      "metadata": {
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          "height": 89
        },
        "id": "FjITVVynEYSQ",
        "outputId": "7af0a79b-81aa-41a7-99d0-cdb6c7a2e97b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   price   latitude  longitude              type\n",
              "0   2090  28.628838 -81.442822  subject property"
            ],
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3c6b1f86-c701-4839-be78-a370d3a21f02')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    background-color: #434B5C;\n",
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              "      let quickchartButtonEl =\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get saels comp attributes into a dataframe\n",
        "df_rent_comp = df_prop_rent_comps[['price', 'latitude', 'longitude']]\n",
        "df_rent_comp['type'] = 'comparables'\n",
        "df_rent_comp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "qshCecLcEYP8",
        "outputId": "2012de70-434d-4191-cd94-3fb9b422cc14"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-32-6f7925050543>:3: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   price   latitude  longitude         type\n",
              "0   1695  28.626034 -81.443057  comparables\n",
              "1   1695  28.626034 -81.443057  comparables\n",
              "2   2235  28.631799 -81.445176  comparables\n",
              "3   2495  28.632086 -81.444345  comparables\n",
              "4   2550  28.631799 -81.445176  comparables\n",
              "5   1875  28.631388 -81.445411  comparables\n",
              "6   1875  28.631388 -81.445411  comparables\n",
              "7   2200  28.626165 -81.439713  comparables\n",
              "8   2295  28.631118 -81.446695  comparables\n",
              "9   1960  28.633249 -81.443056  comparables"
            ],
            "text/html": [
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              "      <th>latitude</th>\n",
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              "      <th>1</th>\n",
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              "      <td>-81.443057</td>\n",
              "      <td>comparables</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2235</td>\n",
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              "      <td>-81.445176</td>\n",
              "      <td>comparables</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2495</td>\n",
              "      <td>28.632086</td>\n",
              "      <td>-81.444345</td>\n",
              "      <td>comparables</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2550</td>\n",
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              "      <td>-81.445176</td>\n",
              "      <td>comparables</td>\n",
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              "      <th>5</th>\n",
              "      <td>1875</td>\n",
              "      <td>28.631388</td>\n",
              "      <td>-81.445411</td>\n",
              "      <td>comparables</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1875</td>\n",
              "      <td>28.631388</td>\n",
              "      <td>-81.445411</td>\n",
              "      <td>comparables</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2200</td>\n",
              "      <td>28.626165</td>\n",
              "      <td>-81.439713</td>\n",
              "      <td>comparables</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2295</td>\n",
              "      <td>28.631118</td>\n",
              "      <td>-81.446695</td>\n",
              "      <td>comparables</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1960</td>\n",
              "      <td>28.633249</td>\n",
              "      <td>-81.443056</td>\n",
              "      <td>comparables</td>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# concatenate subj & comps dataframe\n",
        "df_rent_comps_fig = pd.concat([df_rent_comp_subj, df_rent_comp])\n",
        "# plot coordinates in map\n",
        "fig = px.scatter_mapbox(df_rent_comps_fig, lat=\"latitude\", lon=\"longitude\", size=\"price\", color=\"type\", zoom=14, title=f\"<b>{address}</b><br>Property Rental Comparables\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "zUdJB7r3Ewv3",
        "outputId": "7aff5de7-0bf4-4ac4-ec92-906fc541242a"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4) Market Statistics 📊\n",
        "[Api Docs](https://developers.rentcast.io/reference/rent-estimate-long-term) | Returns aggregate rental statistics and listing trends for a single US zip code."
      ],
      "metadata": {
        "id": "iW5dednjX-V5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get zip code from address\n",
        "zip_code = address.split(' ')[-1]\n",
        "zip_code"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "H9Wu50QuGZV5",
        "outputId": "d86e7d9f-8347-4f3b-b5bf-0e847bf35bd6"
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      "execution_count": 34,
      "outputs": [
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          "data": {
            "text/plain": [
              "'32810'"
            ],
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            }
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          "metadata": {},
          "execution_count": 34
        }
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    {
      "cell_type": "code",
      "source": [
        "# get market data from rent cast\n",
        "url = \"https://api.rentcast.io/v1/markets\"\n",
        "querystring = {\n",
        "    \"zipCode\": zip_code,\n",
        "    \"historyRange\": 24 # number of months\n",
        "}\n",
        "headers = {\n",
        "    \"accept\": \"application/json\",\n",
        "    \"X-Api-Key\": rent_cast_api_key\n",
        "}\n",
        "response_market_stats = requests.get(url, headers=headers, params=querystring)\n",
        "response_market_stats.text"
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              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:750,\"maxRent\":52000,\"totalListings\":465,\"dataByBedrooms\":[{\"bedrooms\":0,\"averageRent\":8778.29,\"minRent\":1275,\"maxRent\":52000,\"totalListings\":7},{\"bedrooms\":1,\"averageRent\":1654.43,\"minRent\":750,\"maxRent\":2070,\"totalListings\":159},{\"bedrooms\":2,\"averageRent\":1959.31,\"minRent\":900,\"maxRent\":2799,\"totalListings\":162},{\"bedrooms\":3,\"averageRent\":2223.81,\"minRent\":847,\"maxRent\":3156,\"totalListings\":113},{\"bedrooms\":4,\"averageRent\":2208.1,\"minRent\":1650,\"maxRent\":3193,\"totalListings\":20},{\"bedrooms\":5,\"averageRent\":2266.67,\"minRent\":1360,\"maxRent\":2745,\"totalListings\":3},{\"bedrooms\":6,\"averageRent\":3575,\"minRent\":3575,\"maxRent\":3575,\"totalListings\":1}]},\"2022-10\":{\"averageRent\":3192.24,\"minRent\":690,\"maxRent\":52000,\"totalListings\":503,\"dataByBedrooms\":[{\"bedrooms\":0,\"averageRent\":8778.29,\"minRent\":1275,\"maxRent\":52000,\"totalListings\":7},{\"bedrooms\":1,\"averageRent\":1604.21,\"minRent\":690,\"maxRent\":2101,\"totalListings\":178},{\"bedrooms\":2,\"averageRent\":1930.82,\"minRent\":900,\"maxRent\":2653,\"totalListings\":166},{\"bedrooms\":3,\"averageRent\":2154.23,\"minRent\":847,\"maxRent\":2790,\"totalListings\":123},{\"bedrooms\":4,\"averageRent\":2325.65,\"minRent\":1650,\"maxRent\":3193,\"totalListings\":26},{\"bedrooms\":5,\"averageRent\":2052.5,\"minRent\":1360,\"maxRent\":2745,\"totalListings\":2},{\"bedrooms\":6,\"averageRent\":3500,\"minRent\":3500,\"maxRent\":3500,\"totalListings\":1}]},\"2022-11\":{\"averageRent\":3153.96,\"minRent\":775,\"maxRent\":52000,\"totalListings\":529,\"dataByBedrooms\":[{\"bedrooms\":0,\"averageRent\":8778.29,\"minRent\":1275,\"maxRent\":52000,\"totalListings\":7},{\"bedrooms\":1,\"averageRent\":1606.24,\"minRent\":775,\"maxRent\":2070,\"totalListings\":190},{\"bedrooms\":2,\"averageRent\":1924.5,\"minRent\":900,\"maxRent\":2653,\"totalListings\":165},{\"bedrooms\":3,\"averageRent\":2162.01,\"minRent\":847,\"maxRent\":3305,\"totalListings\":135},{\"bedrooms\":4,\"averageRent\":2371.68,\"minRent\":1650,\"maxRent\":3193,\"totalListings\":28},{\"bedrooms\":5,\"averageRent\":2235,\"minRent\":1360,\"maxRent\":2745,\"totalListings\":3},{\"bedrooms\":6,\"averageRent\":3000,\"minRent\":3000,\"maxRent\":3000,\"totalListings\":1}]},\"2022-12\":{\"averageRent\":3252.11,\"minRent\":775,\"maxRent\":55000,\"totalListings\":512,\"dataByBedrooms\":[{\"bedrooms\":0,\"averageRent\":9206.86,\"minRent\":1275,\"maxRent\":55000,\"totalListings\":7},{\"bedrooms\":1,\"averageRent\":1618.95,\"minRent\":775,\"maxRent\":2708,\"totalListings\":182},{\"bedrooms\":2,\"averageRent\":1886.76,\"minRent\":900,\"maxRent\":2653,\"totalListings\":160},{\"bedrooms\":3,\"averageRent\":2161.4,\"minRent\":847,\"maxRent\":3340,\"totalListings\":135},{\"bedrooms\":4,\"averageRent\":2377.46,\"minRent\":1795,\"maxRent\":3000,\"totalListings\":24},{\"bedrooms\":5,\"averageRent\":2513.33,\"minRent\":2195,\"maxRent\":2745,\"totalListings\":3},{\"bedrooms\":6,\"averageRent\":3000,\"minRent\":3000,\"maxRent\":3000,\"totalListings\":1}]},\"2023-01\":{\"averageRent\":2150,\"minRent\":775,\"maxRent\":3473,\"totalListings\":524,\"dataByBedrooms\":[{\"bedrooms\":0,\"averageRent\":1540,\"minRent\":1275,\"maxRent\":1800,\"totalListings\":8},{\"bedrooms\":1,\"averageRent\":1623,\"minRent\":775,\"maxRent\":2708,\"totalListings\":186},{\"bedrooms\":2,\"averageRent\":1915,\"minRent\":900,\"maxRent\":2890,\"totalListings\":166},{\"bedrooms\":3,\"averageRent\":2139,\"minRent\":847,\"maxRent\":3473,\"totalListings\":143},{\"bedrooms\":4,\"averageRent\":2435,\"minRent\":1850,\"maxRent\":3000,\"totalListings\":18},{\"bedrooms\":5,\"averageRent\":2398,\"minRent\":2195,\"maxRent\":2600,\"totalListings\":2},{\"bedrooms\":6,\"averageRent\":3000,\"minRent\":3000,\"maxRent\":3000,\"totalListings\":1}]},\"2023-02\":{\"averageRent\":2116,\"minRent\":775,\"maxRent\":3517,\"totalListings\":528,\"dataByBedrooms\":[{\"bedrooms\":0,\"averageRent\":1535,\"minRent\":1275,\"maxRent\":1800,\"totalListings\":7},{\"bedrooms\":1,\"averageRent\":1671,\"minRent\":775,\"maxRent\":2708,\"totalListings\":176},{\"bedrooms\":2,\"averageRent\":1944,\"minRent\":900,\"maxRent\":2673,\"totalListings\":161},{\"bedrooms\":3,\"averageRent\":2167,\"minRent\":847,\"maxRent\":3517,\"totalListings\":153},{\"bedrooms\":4,\"averageRent\":2184,\"minRent\":950,\"maxRent\":2850,\"totalListings\":26},{\"bedrooms\":5,\"averageRent\":2309,\"minRent\":2095,\"maxRent\":2600,\"totalListings\":4},{\"bedrooms\":6,\"averageRent\":3000,\"minRent\":3000,\"maxRent\":3000,\"totalListings\":1}]},\"2023-03\":{\"averageRent\":1954,\"minRent\":775,\"maxRent\":3473,\"totalListings\":533,\"dataByBedrooms\":[{\"bedrooms\":1,\"averageRent\":1700,\"minRent\":775,\"maxRent\":2708,\"totalListings\":175},{\"bedrooms\":2,\"averageRent\":1970,\"minRent\":900,\"maxRent\":2673,\"totalListings\":159},{\"bedrooms\":3,\"averageRent\":2168,\"minRent\":847,\"maxRent\":3473,\"totalListings\":165},{\"bedrooms\":4,\"aver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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get all dates for market stats to a list\n",
        "market_date_list = list(response_market_stats.json()['rentalData']['history'].keys())\n",
        "market_date_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_BPFzy1JHX5S",
        "outputId": "588f4c86-0950-45f6-9b94-6f24b9dc059f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['2021-11',\n",
              " '2021-12',\n",
              " '2021-10',\n",
              " '2021-09',\n",
              " '2022-01',\n",
              " '2022-02',\n",
              " '2022-03',\n",
              " '2022-04',\n",
              " '2022-05',\n",
              " '2022-06',\n",
              " '2022-07',\n",
              " '2022-08',\n",
              " '2022-09',\n",
              " '2022-10',\n",
              " '2022-11',\n",
              " '2022-12',\n",
              " '2023-01',\n",
              " '2023-02',\n",
              " '2023-03',\n",
              " '2023-04',\n",
              " '2023-05',\n",
              " '2023-06',\n",
              " '2023-07',\n",
              " '2023-08']"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get rental details for latest date\n",
        "response_market_stats.json()['rentalData']['history'][market_date_list[-1]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FEh48D-JHiZh",
        "outputId": "7184b753-d4bb-45f7-b9fb-742d7135d999"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'averageRent': 1991,\n",
              " 'minRent': 850,\n",
              " 'maxRent': 10000,\n",
              " 'totalListings': 501,\n",
              " 'dataByBedrooms': [{'bedrooms': 1,\n",
              "   'averageRent': 1676,\n",
              "   'minRent': 850,\n",
              "   'maxRent': 2118,\n",
              "   'totalListings': 140},\n",
              "  {'bedrooms': 2,\n",
              "   'averageRent': 1982,\n",
              "   'minRent': 1350,\n",
              "   'maxRent': 2743,\n",
              "   'totalListings': 161},\n",
              "  {'bedrooms': 3,\n",
              "   'averageRent': 2205,\n",
              "   'minRent': 1065,\n",
              "   'maxRent': 10000,\n",
              "   'totalListings': 163},\n",
              "  {'bedrooms': 4,\n",
              "   'averageRent': 2302,\n",
              "   'minRent': 1595,\n",
              "   'maxRent': 3250,\n",
              "   'totalListings': 34},\n",
              "  {'bedrooms': 5,\n",
              "   'averageRent': 2023,\n",
              "   'minRent': 1925,\n",
              "   'maxRent': 2150,\n",
              "   'totalListings': 3}]}"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get market rental history to a dataframe\n",
        "df_market_list = [] # create empty list\n",
        "\n",
        "# iterate through all dates\n",
        "for dt in market_date_list:\n",
        "  # transform month/year rental history to a single dataframe\n",
        "  _df_market = pd.DataFrame(response_market_stats.json()['rentalData']['history'][dt]['dataByBedrooms'])\n",
        "  _df_market['date'] = dt # add date\n",
        "  _df_market.insert(0, 'date', _df_market.pop('date')) # move date to be first column\n",
        "  df_market_list.append(_df_market) # append to list\n",
        "\n",
        "# merge all rental history dates\n",
        "df_market_by_dt = pd.concat(df_market_list)\n",
        "df_market_by_dt"
      ],
      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "eXBQmx6sHfBa",
        "outputId": "1e891921-debe-4472-eb08-97fc1c15dac8"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       date  bedrooms  averageRent  minRent  maxRent  totalListings\n",
              "0   2021-11         0      1471.29     1200     1680              7\n",
              "1   2021-11         1      1696.78      995     2085             63\n",
              "2   2021-11         2      1850.00     1025     2515             65\n",
              "3   2021-11         3      1777.59      783     3154             49\n",
              "4   2021-11         4      1999.50     1400     2500             10\n",
              "..      ...       ...          ...      ...      ...            ...\n",
              "0   2023-08         1      1676.00      850     2118            140\n",
              "1   2023-08         2      1982.00     1350     2743            161\n",
              "2   2023-08         3      2205.00     1065    10000            163\n",
              "3   2023-08         4      2302.00     1595     3250             34\n",
              "4   2023-08         5      2023.00     1925     2150              3\n",
              "\n",
              "[157 rows x 6 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>averageRent</th>\n",
              "      <th>minRent</th>\n",
              "      <th>maxRent</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-11</td>\n",
              "      <td>0</td>\n",
              "      <td>1471.29</td>\n",
              "      <td>1200</td>\n",
              "      <td>1680</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-11</td>\n",
              "      <td>1</td>\n",
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              "      <td>995</td>\n",
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              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-11</td>\n",
              "      <td>2</td>\n",
              "      <td>1850.00</td>\n",
              "      <td>1025</td>\n",
              "      <td>2515</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-11</td>\n",
              "      <td>3</td>\n",
              "      <td>1777.59</td>\n",
              "      <td>783</td>\n",
              "      <td>3154</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-11</td>\n",
              "      <td>4</td>\n",
              "      <td>1999.50</td>\n",
              "      <td>1400</td>\n",
              "      <td>2500</td>\n",
              "      <td>10</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-08</td>\n",
              "      <td>1</td>\n",
              "      <td>1676.00</td>\n",
              "      <td>850</td>\n",
              "      <td>2118</td>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-08</td>\n",
              "      <td>2</td>\n",
              "      <td>1982.00</td>\n",
              "      <td>1350</td>\n",
              "      <td>2743</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-08</td>\n",
              "      <td>3</td>\n",
              "      <td>2205.00</td>\n",
              "      <td>1065</td>\n",
              "      <td>10000</td>\n",
              "      <td>163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-08</td>\n",
              "      <td>4</td>\n",
              "      <td>2302.00</td>\n",
              "      <td>1595</td>\n",
              "      <td>3250</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-08</td>\n",
              "      <td>5</td>\n",
              "      <td>2023.00</td>\n",
              "      <td>1925</td>\n",
              "      <td>2150</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>157 rows × 6 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# filter on number of bedrooms\n",
        "df_market_by_dt_fig = df_market_by_dt.loc[df_market_by_dt[\"bedrooms\"].isin([1,2,3,4])]\n",
        "df_market_by_dt_fig = df_market_by_dt_fig.sort_values(by=[\"date\"]) # sort before plotting\n",
        "# create figure\n",
        "fig = px.line(df_market_by_dt_fig, x=\"date\", y=\"averageRent\", color='bedrooms', title=f\"Average Rent by Bedroom for <b>{zip_code}</b>\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Gda3U1R1Hh-s",
        "outputId": "9a9a9b69-6afb-4b67-fc6f-37cdb06f3da4"
      },
      "execution_count": 39,
      "outputs": [
        {
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          "data": {
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    {
      "cell_type": "code",
      "source": [
        "# get yoy change for average rent\n",
        "df_market_by_dt_yoy_fig = df_market_by_dt_fig.copy()\n",
        "df_market_by_dt_yoy_fig['rent_pct_yoy'] = df_market_by_dt_yoy_fig['averageRent'].pct_change(12)\n",
        "# create figure\n",
        "fig = px.line(df_market_by_dt_yoy_fig, x=\"date\", y=\"rent_pct_yoy\", color='bedrooms', title=f\"YoY Average Rent by Bedroom for <b>{zip_code}</b>\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "yi04v4LyJipd",
        "outputId": "108869eb-e572-4bc3-ac01-b1cc21d3224a"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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