REICalculations_CashFlow.ipynb

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      "name": "REICalculations_CashFlow.ipynb",
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      "source": [
        "# Real Estate Investment Calculations - Cash Flow\n",
        "\n",
        "## Overview\n",
        "| Detail Tag            | Information                                                                                        |\n",
        "|-----------------------|----------------------------------------------------------------------------------------------------|\n",
        "| Originally Created By | Ariel Herrera arielherrera@analyticsariel.com                                                      |\n",
        "| External References   | None |\n",
        "| Input Datasets        | GitHub csv                                                               |\n",
        "| Output Datasets       | Cash Flow Map |\n",
        "| Input Data Source     | DataFrame |\n",
        "| Output Data Source    | Plotly Map |\n",
        "\n",
        "## History\n",
        "| Date         | Developed By  | Reason                                                |\n",
        "|--------------|---------------|-------------------------------------------------------|\n",
        "| 5th April 2022 | Ariel Herrera | Create notebook. |\n",
        "\n",
        "## Getting Started\n",
        "1. Copy this notebook -> File -> Save a Copy in Drive\n",
        "\n",
        "## Useful Resources\n",
        "- [Google Colab Cheat Sheet](https://towardsdatascience.com/cheat-sheet-for-google-colab-63853778c093)\n",
        "- [Cash Flow Rental Charts](https://sparkrental.com/visualizing-rental-cash-flow-4-graphs-showing-how-landlords-money-moves/)\n",
        "- [Plotly Sankey Diagram](https://plotly.com/python/sankey-diagram/)"
      ],
      "metadata": {
        "id": "-ttf0vfi1xoD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"blue\">Install Packages</font>"
      ],
      "metadata": {
        "id": "bWCg0m5xODxz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install plotly -q"
      ],
      "metadata": {
        "id": "VTkXGmuuOEYn"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"blue\">Import Libraries</font>"
      ],
      "metadata": {
        "id": "WuBqVOIiOFjt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "YVI-8dyQOHt8"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"blue\">Data</font>"
      ],
      "metadata": {
        "id": "PpB60cpoO4gk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/analyticsariel/public-data/main/cash_flow_data.csv')\n",
        "df.head()"
      ],
      "metadata": {
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               property_address  rent  additional_income  \\\n",
              "0  123 Main St, Tampa, FL 33603  2600                 50   \n",
              "\n",
              "   principal_and_interest  taxes  insurance  property_management  cap_ex  \\\n",
              "0                    1159    528         80                  234     130   \n",
              "\n",
              "   vacancy  utilities  additional_expenses  \n",
              "0      208         50                   75  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>property_address</th>\n",
              "      <th>rent</th>\n",
              "      <th>additional_income</th>\n",
              "      <th>principal_and_interest</th>\n",
              "      <th>taxes</th>\n",
              "      <th>insurance</th>\n",
              "      <th>property_management</th>\n",
              "      <th>cap_ex</th>\n",
              "      <th>vacancy</th>\n",
              "      <th>utilities</th>\n",
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              "      <td>123 Main St, Tampa, FL 33603</td>\n",
              "      <td>2600</td>\n",
              "      <td>50</td>\n",
              "      <td>1159</td>\n",
              "      <td>528</td>\n",
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              "      <td>234</td>\n",
              "      <td>130</td>\n",
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              "      <td>75</td>\n",
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      "cell_type": "code",
      "source": [
        "# assign variables to values in table\n",
        "property_address = df['property_address'].iloc[0]\n",
        "rent = df['rent'].iloc[0]\n",
        "additional_income = df['additional_income'].iloc[0]\n",
        "principal_and_interest = df['principal_and_interest'].iloc[0]\n",
        "taxes = df['taxes'].iloc[0]\n",
        "insurance = df['insurance'].iloc[0]\n",
        "property_management = df['property_management'].iloc[0]\n",
        "cap_ex = df['cap_ex'].iloc[0]\n",
        "vacancy = df['vacancy'].iloc[0]\n",
        "utilities = df['utilities'].iloc[0]\n",
        "additional_expenses = df['additional_expenses'].iloc[0]\n",
        "\n",
        "# summary variables\n",
        "total_income = rent + additional_income\n",
        "fixed_expense = principal_and_interest + taxes + insurance\n",
        "variable_expense = property_management + cap_ex + vacancy + utilities + additional_expenses\n",
        "total_expense = fixed_expense + variable_expense"
      ],
      "metadata": {
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      "execution_count": 4,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"blue\">Transform</font>"
      ],
      "metadata": {
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      }
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    {
      "cell_type": "code",
      "source": [
        "# create list of labels for the chart\n",
        "label_list = [ \"Income\",\n",
        "               \"Rent\", \n",
        "               \"Additional Income\",\n",
        "               \"Expense\", \n",
        "               \"Fixed\",\n",
        "               \"Variable\",\n",
        "               \"Principal & Interest\",\n",
        "               \"Taxes\",\n",
        "               \"Insurance\", \n",
        "               \"Property Management\", \n",
        "               \"CapEx\",\n",
        "               \"Vacancy\",\n",
        "               \"Utilities\",\n",
        "               \"Additional Expenses\",\n",
        "               ]\n",
        "# append an index number to each element in the list\n",
        "label_dict = {}\n",
        "for i in range(len(label_list)):\n",
        "  label_dict[label_list[i]] = i\n",
        "label_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIkINZe2X0tA",
        "outputId": "18dcafe9-ed27-42df-902c-952fd4652665"
      },
      "execution_count": 5,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Additional Expenses': 13,\n",
              " 'Additional Income': 2,\n",
              " 'CapEx': 10,\n",
              " 'Expense': 3,\n",
              " 'Fixed': 4,\n",
              " 'Income': 0,\n",
              " 'Insurance': 8,\n",
              " 'Principal & Interest': 6,\n",
              " 'Property Management': 9,\n",
              " 'Rent': 1,\n",
              " 'Taxes': 7,\n",
              " 'Utilities': 12,\n",
              " 'Vacancy': 11,\n",
              " 'Variable': 5}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create sub list for each node -> target -> value\n",
        "sankey_list = [\n",
        "  # income\n",
        "  [\"Income\", \"Rent\", rent], \n",
        "  [\"Income\", \"Additional Income\", additional_income], \n",
        "  # expenses [fixed]\n",
        "  [\"Expense\", \"Fixed\", fixed_expense], \n",
        "  [\"Fixed\", \"Principal & Interest\", principal_and_interest], \n",
        "  [\"Fixed\", \"Taxes\", taxes], \n",
        "  [\"Fixed\", \"Insurance\", insurance],\n",
        "  # expenses [variable]\n",
        "  [\"Expense\", \"Variable\", variable_expense], \n",
        "  [\"Variable\", \"Property Management\", property_management], \n",
        "  [\"Variable\", \"CapEx\", cap_ex], \n",
        "  [\"Variable\", \"Vacancy\", vacancy], \n",
        "  [\"Variable\", \"Utilities\", utilities], \n",
        "  [\"Variable\", \"Additional Expenses\", additional_expenses],\n",
        "]"
      ],
      "metadata": {
        "id": "qM84BTATLcwF"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create sublists from the sankey list\n",
        "source_list = [label_dict[x[0]] for x in sankey_list]\n",
        "target_list = [label_dict[x[1]] for x in sankey_list]\n",
        "value_list = [x[2] for x in sankey_list]\n",
        "\n",
        "print('Source list:', source_list)\n",
        "print('Target list:', target_list)\n",
        "print('Value list:', value_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Ra6NQQHZCqD",
        "outputId": "854950de-9111-4152-88de-b84dec49e8d6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Source list: [0, 0, 3, 4, 4, 4, 3, 5, 5, 5, 5, 5]\n",
            "Target list: [1, 2, 4, 6, 7, 8, 5, 9, 10, 11, 12, 13]\n",
            "Value list: [2600, 50, 1767, 1159, 528, 80, 697, 234, 130, 208, 50, 75]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"blue\">Visualization</font>"
      ],
      "metadata": {
        "id": "YnYhmFFsOOII"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure(go.Indicator(\n",
        "    mode = \"number+delta\",\n",
        "    value = total_income - total_expense,\n",
        "    number = {'prefix': \"Cash Flow - $\"},\n",
        "    domain = {'x': [0, 1], 'y': [0, 1]})\n",
        ")\n",
        "\n",
        "fig.update_layout(height=300)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "Q6r7nNG0cKBN",
        "outputId": "9e4c3d16-827e-48f3-bda6-907eb0d80277"
      },
      "execution_count": 8,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "# create figure\n",
        "fig = go.Figure(data=[go.Sankey(\n",
        "    # nodes\n",
        "    node = dict(\n",
        "      pad = 15,\n",
        "      thickness = 20,\n",
        "      line = dict(color = \"black\", width = 0.5),\n",
        "      label = label_list, # cash flow parameters\n",
        "      hovertemplate='%{label} has total value $%{value}',\n",
        "    ),\n",
        "    # links\n",
        "    link = dict(\n",
        "      source = source_list, # start node\n",
        "      target = target_list, # end node\n",
        "      value = value_list # value of end node\n",
        "  ))])\n",
        "\n",
        "fig.update_layout(\n",
        "    hovermode = 'x',\n",
        "    title=\"Income & Expenses<br>Property Address: {}\".format(property_address),\n",
        "    # optional for black background\n",
        "    font=dict(size = 10, color = 'white'),\n",
        "    plot_bgcolor='black',\n",
        "    paper_bgcolor='black'\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "vpkbKnt2LEtm",
        "outputId": "633a6a9f-8703-4ed9-ded5-61324001fd15"
      },
      "execution_count": 9,
      "outputs": [
        {
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    {
      "cell_type": "markdown",
      "source": [
        "# End Notebook"
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        "id": "F-_0rUGqOY4O"
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}