refactor: multi-dashboard structural migration

- Rename dbt project from toronto_housing to portfolio
- Restructure dbt models into domain subdirectories:
  - shared/ for cross-domain dimensions (dim_time)
  - staging/toronto/, intermediate/toronto/, marts/toronto/
- Update SQLAlchemy models for raw_toronto schema
- Add explicit cross-schema FK relationships for FactRentals
- Namespace figure factories under figures/toronto/
- Namespace notebooks under notebooks/toronto/
- Update Makefile with domain-specific targets and env loading
- Update all documentation for multi-dashboard structure

This enables adding new dashboard projects (e.g., /football, /energy)
without structural conflicts or naming collisions.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

notebooks/toronto/housing/rent_trend_line.ipynb

@@ -0,0 +1,200 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Rent Trend Line Chart\n",
+    "\n",
+    "Shows 5-year rental price trends across Toronto neighbourhoods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Data Reference\n",
+    "\n",
+    "### Source Tables\n",
+    "\n",
+    "| Table | Grain | Key Columns |\n",
+    "|-------|-------|-------------|\n",
+    "| `mart_neighbourhood_housing` | neighbourhood × year | year, avg_rent_2bed, rent_yoy_change_pct |\n",
+    "\n",
+    "### SQL Query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# Load .env from project root\n",
+    "load_dotenv(\"../../.env\")\n",
+    "\n",
+    "engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
+    "\n",
+    "# City-wide average rent by year\n",
+    "query = \"\"\"\n",
+    "SELECT\n",
+    "    year,\n",
+    "    AVG(avg_rent_bachelor) as avg_rent_bachelor,\n",
+    "    AVG(avg_rent_1bed) as avg_rent_1bed,\n",
+    "    AVG(avg_rent_2bed) as avg_rent_2bed,\n",
+    "    AVG(avg_rent_3bed) as avg_rent_3bed,\n",
+    "    AVG(rent_yoy_change_pct) as avg_yoy_change\n",
+    "FROM public_marts.mart_neighbourhood_housing\n",
+    "WHERE year >= (SELECT MAX(year) - 5 FROM public_marts.mart_neighbourhood_housing)\n",
+    "GROUP BY year\n",
+    "ORDER BY year\n",
+    "\"\"\"\n",
+    "\n",
+    "df = pd.read_sql(query, engine)\n",
+    "print(f\"Loaded {len(df)} years of rent data\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transformation Steps\n",
+    "\n",
+    "1. Aggregate rent by year (city-wide average)\n",
+    "2. Convert year to datetime for proper x-axis\n",
+    "3. Reshape for multi-line chart by bedroom type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create date column from year\n",
+    "df[\"date\"] = pd.to_datetime(df[\"year\"].astype(str) + \"-01-01\")\n",
+    "\n",
+    "# Melt for multi-line chart\n",
+    "df_melted = df.melt(\n",
+    "    id_vars=[\"year\", \"date\"],\n",
+    "    value_vars=[\"avg_rent_bachelor\", \"avg_rent_1bed\", \"avg_rent_2bed\", \"avg_rent_3bed\"],\n",
+    "    var_name=\"bedroom_type\",\n",
+    "    value_name=\"avg_rent\",\n",
+    ")\n",
+    "\n",
+    "# Clean labels\n",
+    "df_melted[\"bedroom_type\"] = df_melted[\"bedroom_type\"].map(\n",
+    "    {\n",
+    "        \"avg_rent_bachelor\": \"Bachelor\",\n",
+    "        \"avg_rent_1bed\": \"1 Bedroom\",\n",
+    "        \"avg_rent_2bed\": \"2 Bedroom\",\n",
+    "        \"avg_rent_3bed\": \"3 Bedroom\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sample Output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\n",
+    "    [\n",
+    "        \"year\",\n",
+    "        \"avg_rent_bachelor\",\n",
+    "        \"avg_rent_1bed\",\n",
+    "        \"avg_rent_2bed\",\n",
+    "        \"avg_rent_3bed\",\n",
+    "        \"avg_yoy_change\",\n",
+    "    ]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Data Visualization\n",
+    "\n",
+    "### Figure Factory\n",
+    "\n",
+    "Uses `create_price_time_series` from `portfolio_app.figures.toronto.time_series`.\n",
+    "\n",
+    "**Key Parameters:**\n",
+    "- `date_column`: 'date'\n",
+    "- `price_column`: 'avg_rent'\n",
+    "- `group_column`: 'bedroom_type' (for multi-line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "sys.path.insert(0, \"../..\")\n",
+    "\n",
+    "from portfolio_app.figures.toronto.time_series import create_price_time_series\n",
+    "\n",
+    "data = df_melted.to_dict(\"records\")\n",
+    "\n",
+    "fig = create_price_time_series(\n",
+    "    data=data,\n",
+    "    date_column=\"date\",\n",
+    "    price_column=\"avg_rent\",\n",
+    "    group_column=\"bedroom_type\",\n",
+    "    title=\"Toronto Average Rent Trend (5 Years)\",\n",
+    ")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### YoY Change Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show year-over-year changes\n",
+    "print(\"Year-over-Year Rent Change (%)\")\n",
+    "df[[\"year\", \"avg_yoy_change\"]].dropna()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}