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>

portfolio_app/figures/toronto/__init__.py

@@ -0,0 +1,61 @@
+"""Plotly figure factories for Toronto dashboard visualizations."""
+
+from .bar_charts import (
+    create_horizontal_bar,
+    create_ranking_bar,
+    create_stacked_bar,
+)
+from .choropleth import (
+    create_choropleth_figure,
+    create_zone_map,
+)
+from .demographics import (
+    create_age_pyramid,
+    create_donut_chart,
+    create_income_distribution,
+)
+from .radar import (
+    create_comparison_radar,
+    create_radar_figure,
+)
+from .scatter import (
+    create_bubble_chart,
+    create_scatter_figure,
+)
+from .summary_cards import create_metric_card_figure, create_summary_metrics
+from .time_series import (
+    add_policy_markers,
+    create_market_comparison_chart,
+    create_price_time_series,
+    create_time_series_with_events,
+    create_volume_time_series,
+)
+
+__all__ = [
+    # Choropleth
+    "create_choropleth_figure",
+    "create_zone_map",
+    # Time series
+    "create_price_time_series",
+    "create_volume_time_series",
+    "create_market_comparison_chart",
+    "create_time_series_with_events",
+    "add_policy_markers",
+    # Summary
+    "create_metric_card_figure",
+    "create_summary_metrics",
+    # Bar charts
+    "create_ranking_bar",
+    "create_stacked_bar",
+    "create_horizontal_bar",
+    # Scatter plots
+    "create_scatter_figure",
+    "create_bubble_chart",
+    # Radar charts
+    "create_radar_figure",
+    "create_comparison_radar",
+    # Demographics
+    "create_age_pyramid",
+    "create_donut_chart",
+    "create_income_distribution",
+]

portfolio_app/figures/toronto/bar_charts.py

@@ -0,0 +1,238 @@
+"""Bar chart figure factories for dashboard visualizations."""
+
+from typing import Any
+
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+
+
+def create_ranking_bar(
+    data: list[dict[str, Any]],
+    name_column: str,
+    value_column: str,
+    title: str | None = None,
+    top_n: int = 10,
+    bottom_n: int = 10,
+    color_top: str = "#4CAF50",
+    color_bottom: str = "#F44336",
+    value_format: str = ",.0f",
+) -> go.Figure:
+    """Create horizontal bar chart showing top and bottom rankings.
+
+    Args:
+        data: List of data records.
+        name_column: Column name for labels.
+        value_column: Column name for values.
+        title: Optional chart title.
+        top_n: Number of top items to show.
+        bottom_n: Number of bottom items to show.
+        color_top: Color for top performers.
+        color_bottom: Color for bottom performers.
+        value_format: Number format string for values.
+
+    Returns:
+        Plotly Figure object.
+    """
+    if not data:
+        return _create_empty_figure(title or "Rankings")
+
+    df = pd.DataFrame(data).sort_values(value_column, ascending=False)
+
+    # Get top and bottom
+    top_df = df.head(top_n).copy()
+    bottom_df = df.tail(bottom_n).copy()
+
+    top_df["group"] = "Top"
+    bottom_df["group"] = "Bottom"
+
+    # Combine with gap in the middle
+    combined = pd.concat([top_df, bottom_df])
+    combined["color"] = combined["group"].map(
+        {"Top": color_top, "Bottom": color_bottom}
+    )
+
+    fig = go.Figure()
+
+    # Add top bars
+    fig.add_trace(
+        go.Bar(
+            y=top_df[name_column],
+            x=top_df[value_column],
+            orientation="h",
+            marker_color=color_top,
+            name="Top",
+            text=top_df[value_column].apply(lambda x: f"{x:{value_format}}"),
+            textposition="auto",
+            hovertemplate=f"%{{y}}<br>{value_column}: %{{x:{value_format}}}<extra></extra>",
+        )
+    )
+
+    # Add bottom bars
+    fig.add_trace(
+        go.Bar(
+            y=bottom_df[name_column],
+            x=bottom_df[value_column],
+            orientation="h",
+            marker_color=color_bottom,
+            name="Bottom",
+            text=bottom_df[value_column].apply(lambda x: f"{x:{value_format}}"),
+            textposition="auto",
+            hovertemplate=f"%{{y}}<br>{value_column}: %{{x:{value_format}}}<extra></extra>",
+        )
+    )
+
+    fig.update_layout(
+        title=title,
+        barmode="group",
+        showlegend=True,
+        legend={"orientation": "h", "yanchor": "bottom", "y": 1.02},
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
+        yaxis={"autorange": "reversed", "title": None},
+        margin={"l": 10, "r": 10, "t": 40, "b": 10},
+    )
+
+    return fig
+
+
+def create_stacked_bar(
+    data: list[dict[str, Any]],
+    x_column: str,
+    value_column: str,
+    category_column: str,
+    title: str | None = None,
+    color_map: dict[str, str] | None = None,
+    show_percentages: bool = False,
+) -> go.Figure:
+    """Create stacked bar chart for breakdown visualizations.
+
+    Args:
+        data: List of data records.
+        x_column: Column name for x-axis categories.
+        value_column: Column name for values.
+        category_column: Column name for stacking categories.
+        title: Optional chart title.
+        color_map: Mapping of category to color.
+        show_percentages: Whether to normalize to 100%.
+
+    Returns:
+        Plotly Figure object.
+    """
+    if not data:
+        return _create_empty_figure(title or "Breakdown")
+
+    df = pd.DataFrame(data)
+
+    # Default color scheme
+    if color_map is None:
+        categories = df[category_column].unique()
+        colors = px.colors.qualitative.Set2[: len(categories)]
+        color_map = dict(zip(categories, colors, strict=False))
+
+    fig = px.bar(
+        df,
+        x=x_column,
+        y=value_column,
+        color=category_column,
+        color_discrete_map=color_map,
+        barmode="stack",
+        text=value_column if not show_percentages else None,
+    )
+
+    if show_percentages:
+        fig.update_traces(texttemplate="%{y:.1f}%", textposition="inside")
+
+    fig.update_layout(
+        title=title,
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
+        yaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
+        legend={"orientation": "h", "yanchor": "bottom", "y": 1.02},
+        margin={"l": 10, "r": 10, "t": 60, "b": 10},
+    )
+
+    return fig
+
+
+def create_horizontal_bar(
+    data: list[dict[str, Any]],
+    name_column: str,
+    value_column: str,
+    title: str | None = None,
+    color: str = "#2196F3",
+    value_format: str = ",.0f",
+    sort: bool = True,
+) -> go.Figure:
+    """Create simple horizontal bar chart.
+
+    Args:
+        data: List of data records.
+        name_column: Column name for labels.
+        value_column: Column name for values.
+        title: Optional chart title.
+        color: Bar color.
+        value_format: Number format string.
+        sort: Whether to sort by value descending.
+
+    Returns:
+        Plotly Figure object.
+    """
+    if not data:
+        return _create_empty_figure(title or "Bar Chart")
+
+    df = pd.DataFrame(data)
+
+    if sort:
+        df = df.sort_values(value_column, ascending=True)
+
+    fig = go.Figure(
+        go.Bar(
+            y=df[name_column],
+            x=df[value_column],
+            orientation="h",
+            marker_color=color,
+            text=df[value_column].apply(lambda x: f"{x:{value_format}}"),
+            textposition="outside",
+            hovertemplate=f"%{{y}}<br>Value: %{{x:{value_format}}}<extra></extra>",
+        )
+    )
+
+    fig.update_layout(
+        title=title,
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None},
+        yaxis={"title": None},
+        margin={"l": 10, "r": 10, "t": 40, "b": 10},
+    )
+
+    return fig
+
+
+def _create_empty_figure(title: str) -> go.Figure:
+    """Create an empty figure with a message."""
+    fig = go.Figure()
+    fig.add_annotation(
+        text="No data available",
+        xref="paper",
+        yref="paper",
+        x=0.5,
+        y=0.5,
+        showarrow=False,
+        font={"size": 14, "color": "#888888"},
+    )
+    fig.update_layout(
+        title=title,
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={"visible": False},
+        yaxis={"visible": False},
+    )
+    return fig

portfolio_app/figures/toronto/choropleth.py

@@ -0,0 +1,143 @@
+"""Choropleth map figure factory for Toronto housing data."""
+
+from typing import Any
+
+import plotly.express as px
+import plotly.graph_objects as go
+
+
+def create_choropleth_figure(
+    geojson: dict[str, Any] | None,
+    data: list[dict[str, Any]],
+    location_key: str,
+    color_column: str,
+    hover_data: list[str] | None = None,
+    color_scale: str = "Blues",
+    title: str | None = None,
+    map_style: str = "carto-positron",
+    center: dict[str, float] | None = None,
+    zoom: float = 9.5,
+) -> go.Figure:
+    """Create a choropleth map figure.
+
+    Args:
+        geojson: GeoJSON FeatureCollection for boundaries.
+        data: List of data records with location keys and values.
+        location_key: Column name for location identifier.
+        color_column: Column name for color values.
+        hover_data: Additional columns to show on hover.
+        color_scale: Plotly color scale name.
+        title: Optional chart title.
+        map_style: Mapbox style (carto-positron, open-street-map, etc.).
+        center: Map center coordinates {"lat": float, "lon": float}.
+        zoom: Initial zoom level.
+
+    Returns:
+        Plotly Figure object.
+    """
+    # Default center to Toronto
+    if center is None:
+        center = {"lat": 43.7, "lon": -79.4}
+
+    # Use dark-mode friendly map style by default
+    if map_style == "carto-positron":
+        map_style = "carto-darkmatter"
+
+    # If no geojson provided, create a placeholder map
+    if geojson is None or not data:
+        fig = go.Figure(go.Scattermapbox())
+        fig.update_layout(
+            mapbox={
+                "style": map_style,
+                "center": center,
+                "zoom": zoom,
+            },
+            margin={"l": 0, "r": 0, "t": 40, "b": 0},
+            title=title or "Toronto Housing Map",
+            height=500,
+            paper_bgcolor="rgba(0,0,0,0)",
+            plot_bgcolor="rgba(0,0,0,0)",
+            font_color="#c9c9c9",
+        )
+        fig.add_annotation(
+            text="No geometry data available. Complete QGIS digitization to enable map.",
+            xref="paper",
+            yref="paper",
+            x=0.5,
+            y=0.5,
+            showarrow=False,
+            font={"size": 14, "color": "#888888"},
+        )
+        return fig
+
+    # Create choropleth with data
+    import pandas as pd
+
+    df = pd.DataFrame(data)
+
+    # Use dark-mode friendly map style
+    effective_map_style = (
+        "carto-darkmatter" if map_style == "carto-positron" else map_style
+    )
+
+    fig = px.choropleth_mapbox(
+        df,
+        geojson=geojson,
+        locations=location_key,
+        featureidkey=f"properties.{location_key}",
+        color=color_column,
+        color_continuous_scale=color_scale,
+        hover_data=hover_data,
+        mapbox_style=effective_map_style,
+        center=center,
+        zoom=zoom,
+        opacity=0.7,
+    )
+
+    fig.update_layout(
+        margin={"l": 0, "r": 0, "t": 40, "b": 0},
+        title=title,
+        height=500,
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        coloraxis_colorbar={
+            "title": {
+                "text": color_column.replace("_", " ").title(),
+                "font": {"color": "#c9c9c9"},
+            },
+            "thickness": 15,
+            "len": 0.7,
+            "tickfont": {"color": "#c9c9c9"},
+        },
+    )
+
+    return fig
+
+
+def create_zone_map(
+    zones_geojson: dict[str, Any] | None,
+    rental_data: list[dict[str, Any]],
+    metric: str = "avg_rent",
+) -> go.Figure:
+    """Create choropleth map for CMHC zones.
+
+    Args:
+        zones_geojson: GeoJSON for CMHC zone boundaries.
+        rental_data: Rental statistics by zone.
+        metric: Metric to display (avg_rent, vacancy_rate, etc.).
+
+    Returns:
+        Plotly Figure object.
+    """
+    hover_columns = ["zone_name", "avg_rent", "vacancy_rate", "rental_universe"]
+
+    return create_choropleth_figure(
+        geojson=zones_geojson,
+        data=rental_data,
+        location_key="zone_code",
+        color_column=metric,
+        hover_data=[c for c in hover_columns if c != metric],
+        color_scale="Oranges" if "rent" in metric else "Purples",
+        title="Toronto Rental Market by Zone",
+    )

portfolio_app/figures/toronto/demographics.py

@@ -0,0 +1,240 @@
+"""Demographics-specific chart factories."""
+
+from typing import Any
+
+import pandas as pd
+import plotly.graph_objects as go
+
+
+def create_age_pyramid(
+    data: list[dict[str, Any]],
+    age_groups: list[str],
+    male_column: str = "male",
+    female_column: str = "female",
+    title: str | None = None,
+) -> go.Figure:
+    """Create population pyramid by age and gender.
+
+    Args:
+        data: List with one record per age group containing male/female counts.
+        age_groups: List of age group labels in order (youngest to oldest).
+        male_column: Column name for male population.
+        female_column: Column name for female population.
+        title: Optional chart title.
+
+    Returns:
+        Plotly Figure object.
+    """
+    if not data or not age_groups:
+        return _create_empty_figure(title or "Age Distribution")
+
+    df = pd.DataFrame(data)
+
+    # Ensure data is ordered by age groups
+    if "age_group" in df.columns:
+        df["age_order"] = df["age_group"].apply(
+            lambda x: age_groups.index(x) if x in age_groups else -1
+        )
+        df = df.sort_values("age_order")
+
+    male_values = df[male_column].tolist() if male_column in df.columns else []
+    female_values = df[female_column].tolist() if female_column in df.columns else []
+
+    # Make male values negative for pyramid effect
+    male_values_neg = [-v for v in male_values]
+
+    fig = go.Figure()
+
+    # Male bars (left side, negative values)
+    fig.add_trace(
+        go.Bar(
+            y=age_groups,
+            x=male_values_neg,
+            orientation="h",
+            name="Male",
+            marker_color="#2196F3",
+            hovertemplate="%{y}<br>Male: %{customdata:,}<extra></extra>",
+            customdata=male_values,
+        )
+    )
+
+    # Female bars (right side, positive values)
+    fig.add_trace(
+        go.Bar(
+            y=age_groups,
+            x=female_values,
+            orientation="h",
+            name="Female",
+            marker_color="#E91E63",
+            hovertemplate="%{y}<br>Female: %{x:,}<extra></extra>",
+        )
+    )
+
+    # Calculate max for symmetric axis
+    max_val = max(max(male_values, default=0), max(female_values, default=0))
+
+    fig.update_layout(
+        title=title,
+        barmode="overlay",
+        bargap=0.1,
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={
+            "title": "Population",
+            "gridcolor": "rgba(128,128,128,0.2)",
+            "range": [-max_val * 1.1, max_val * 1.1],
+            "tickvals": [-max_val, -max_val / 2, 0, max_val / 2, max_val],
+            "ticktext": [
+                f"{max_val:,.0f}",
+                f"{max_val / 2:,.0f}",
+                "0",
+                f"{max_val / 2:,.0f}",
+                f"{max_val:,.0f}",
+            ],
+        },
+        yaxis={"title": None, "gridcolor": "rgba(128,128,128,0.2)"},
+        legend={"orientation": "h", "yanchor": "bottom", "y": 1.02},
+        margin={"l": 10, "r": 10, "t": 60, "b": 10},
+    )
+
+    return fig
+
+
+def create_donut_chart(
+    data: list[dict[str, Any]],
+    name_column: str,
+    value_column: str,
+    title: str | None = None,
+    colors: list[str] | None = None,
+    hole_size: float = 0.4,
+) -> go.Figure:
+    """Create donut chart for percentage breakdowns.
+
+    Args:
+        data: List of data records with name and value.
+        name_column: Column name for labels.
+        value_column: Column name for values.
+        title: Optional chart title.
+        colors: List of colors for segments.
+        hole_size: Size of center hole (0-1).
+
+    Returns:
+        Plotly Figure object.
+    """
+    if not data:
+        return _create_empty_figure(title or "Distribution")
+
+    df = pd.DataFrame(data)
+
+    if colors is None:
+        colors = [
+            "#2196F3",
+            "#4CAF50",
+            "#FF9800",
+            "#E91E63",
+            "#9C27B0",
+            "#00BCD4",
+            "#FFC107",
+            "#795548",
+        ]
+
+    fig = go.Figure(
+        go.Pie(
+            labels=df[name_column],
+            values=df[value_column],
+            hole=hole_size,
+            marker_colors=colors[: len(df)],
+            textinfo="percent+label",
+            textposition="outside",
+            hovertemplate="%{label}<br>%{value:,} (%{percent})<extra></extra>",
+        )
+    )
+
+    fig.update_layout(
+        title=title,
+        paper_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        showlegend=False,
+        margin={"l": 10, "r": 10, "t": 60, "b": 10},
+    )
+
+    return fig
+
+
+def create_income_distribution(
+    data: list[dict[str, Any]],
+    bracket_column: str,
+    count_column: str,
+    title: str | None = None,
+    color: str = "#4CAF50",
+) -> go.Figure:
+    """Create histogram-style bar chart for income distribution.
+
+    Args:
+        data: List of data records with income brackets and counts.
+        bracket_column: Column name for income brackets.
+        count_column: Column name for household counts.
+        title: Optional chart title.
+        color: Bar color.
+
+    Returns:
+        Plotly Figure object.
+    """
+    if not data:
+        return _create_empty_figure(title or "Income Distribution")
+
+    df = pd.DataFrame(data)
+
+    fig = go.Figure(
+        go.Bar(
+            x=df[bracket_column],
+            y=df[count_column],
+            marker_color=color,
+            text=df[count_column].apply(lambda x: f"{x:,}"),
+            textposition="outside",
+            hovertemplate="%{x}<br>Households: %{y:,}<extra></extra>",
+        )
+    )
+
+    fig.update_layout(
+        title=title,
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={
+            "title": "Income Bracket",
+            "gridcolor": "rgba(128,128,128,0.2)",
+            "tickangle": -45,
+        },
+        yaxis={
+            "title": "Households",
+            "gridcolor": "rgba(128,128,128,0.2)",
+        },
+        margin={"l": 10, "r": 10, "t": 60, "b": 80},
+    )
+
+    return fig
+
+
+def _create_empty_figure(title: str) -> go.Figure:
+    """Create an empty figure with a message."""
+    fig = go.Figure()
+    fig.add_annotation(
+        text="No data available",
+        xref="paper",
+        yref="paper",
+        x=0.5,
+        y=0.5,
+        showarrow=False,
+        font={"size": 14, "color": "#888888"},
+    )
+    fig.update_layout(
+        title=title,
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={"visible": False},
+        yaxis={"visible": False},
+    )
+    return fig

portfolio_app/figures/toronto/radar.py

@@ -0,0 +1,166 @@
+"""Radar/spider chart figure factory for multi-metric comparison."""
+
+from typing import Any
+
+import plotly.graph_objects as go
+
+
+def create_radar_figure(
+    data: list[dict[str, Any]],
+    metrics: list[str],
+    name_column: str | None = None,
+    title: str | None = None,
+    fill: bool = True,
+    colors: list[str] | None = None,
+) -> go.Figure:
+    """Create radar/spider chart for multi-axis comparison.
+
+    Each record in data represents one entity (e.g., a neighbourhood)
+    with values for each metric that will be plotted on a separate axis.
+
+    Args:
+        data: List of data records, each with values for the metrics.
+        metrics: List of metric column names to display on radar axes.
+        name_column: Column name for entity labels.
+        title: Optional chart title.
+        fill: Whether to fill the radar polygons.
+        colors: List of colors for each data series.
+
+    Returns:
+        Plotly Figure object.
+    """
+    if not data or not metrics:
+        return _create_empty_figure(title or "Radar Chart")
+
+    # Default colors
+    if colors is None:
+        colors = [
+            "#2196F3",
+            "#4CAF50",
+            "#FF9800",
+            "#E91E63",
+            "#9C27B0",
+            "#00BCD4",
+        ]
+
+    fig = go.Figure()
+
+    # Format axis labels
+    axis_labels = [m.replace("_", " ").title() for m in metrics]
+
+    for i, record in enumerate(data):
+        values = [record.get(m, 0) or 0 for m in metrics]
+        # Close the radar polygon
+        values_closed = values + [values[0]]
+        labels_closed = axis_labels + [axis_labels[0]]
+
+        name = (
+            record.get(name_column, f"Series {i + 1}")
+            if name_column
+            else f"Series {i + 1}"
+        )
+        color = colors[i % len(colors)]
+
+        fig.add_trace(
+            go.Scatterpolar(
+                r=values_closed,
+                theta=labels_closed,
+                name=name,
+                line={"color": color, "width": 2},
+                fill="toself" if fill else None,
+                fillcolor=f"rgba{_hex_to_rgba(color, 0.2)}" if fill else None,
+                hovertemplate="%{theta}: %{r:.1f}<extra></extra>",
+            )
+        )
+
+    fig.update_layout(
+        title=title,
+        polar={
+            "radialaxis": {
+                "visible": True,
+                "gridcolor": "rgba(128,128,128,0.3)",
+                "linecolor": "rgba(128,128,128,0.3)",
+                "tickfont": {"color": "#c9c9c9"},
+            },
+            "angularaxis": {
+                "gridcolor": "rgba(128,128,128,0.3)",
+                "linecolor": "rgba(128,128,128,0.3)",
+                "tickfont": {"color": "#c9c9c9"},
+            },
+            "bgcolor": "rgba(0,0,0,0)",
+        },
+        paper_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        showlegend=len(data) > 1,
+        legend={"orientation": "h", "yanchor": "bottom", "y": -0.2},
+        margin={"l": 40, "r": 40, "t": 60, "b": 40},
+    )
+
+    return fig
+
+
+def create_comparison_radar(
+    selected_data: dict[str, Any],
+    average_data: dict[str, Any],
+    metrics: list[str],
+    selected_name: str = "Selected",
+    average_name: str = "City Average",
+    title: str | None = None,
+) -> go.Figure:
+    """Create radar chart comparing a selection to city average.
+
+    Args:
+        selected_data: Data for the selected entity.
+        average_data: Data for the city average.
+        metrics: List of metric column names.
+        selected_name: Label for selected entity.
+        average_name: Label for average.
+        title: Optional chart title.
+
+    Returns:
+        Plotly Figure object.
+    """
+    if not selected_data or not average_data:
+        return _create_empty_figure(title or "Comparison")
+
+    data = [
+        {**selected_data, "__name__": selected_name},
+        {**average_data, "__name__": average_name},
+    ]
+
+    return create_radar_figure(
+        data=data,
+        metrics=metrics,
+        name_column="__name__",
+        title=title,
+        colors=["#4CAF50", "#9E9E9E"],
+    )
+
+
+def _hex_to_rgba(hex_color: str, alpha: float) -> tuple[int, int, int, float]:
+    """Convert hex color to RGBA tuple."""
+    hex_color = hex_color.lstrip("#")
+    r = int(hex_color[0:2], 16)
+    g = int(hex_color[2:4], 16)
+    b = int(hex_color[4:6], 16)
+    return (r, g, b, alpha)
+
+
+def _create_empty_figure(title: str) -> go.Figure:
+    """Create an empty figure with a message."""
+    fig = go.Figure()
+    fig.add_annotation(
+        text="No data available",
+        xref="paper",
+        yref="paper",
+        x=0.5,
+        y=0.5,
+        showarrow=False,
+        font={"size": 14, "color": "#888888"},
+    )
+    fig.update_layout(
+        title=title,
+        paper_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+    )
+    return fig

portfolio_app/figures/toronto/scatter.py

@@ -0,0 +1,184 @@
+"""Scatter plot figure factory for correlation views."""
+
+from typing import Any
+
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+
+
+def create_scatter_figure(
+    data: list[dict[str, Any]],
+    x_column: str,
+    y_column: str,
+    name_column: str | None = None,
+    size_column: str | None = None,
+    color_column: str | None = None,
+    title: str | None = None,
+    x_title: str | None = None,
+    y_title: str | None = None,
+    trendline: bool = False,
+    color_scale: str = "Blues",
+) -> go.Figure:
+    """Create scatter plot for correlation visualization.
+
+    Args:
+        data: List of data records.
+        x_column: Column name for x-axis values.
+        y_column: Column name for y-axis values.
+        name_column: Column name for point labels (hover).
+        size_column: Column name for point sizes.
+        color_column: Column name for color encoding.
+        title: Optional chart title.
+        x_title: X-axis title.
+        y_title: Y-axis title.
+        trendline: Whether to add OLS trendline.
+        color_scale: Plotly color scale for continuous colors.
+
+    Returns:
+        Plotly Figure object.
+    """
+    if not data:
+        return _create_empty_figure(title or "Scatter Plot")
+
+    df = pd.DataFrame(data)
+
+    # Build hover_data
+    hover_data = {}
+    if name_column and name_column in df.columns:
+        hover_data[name_column] = True
+
+    # Create scatter plot
+    fig = px.scatter(
+        df,
+        x=x_column,
+        y=y_column,
+        size=size_column if size_column and size_column in df.columns else None,
+        color=color_column if color_column and color_column in df.columns else None,
+        color_continuous_scale=color_scale,
+        hover_name=name_column,
+        trendline="ols" if trendline else None,
+        opacity=0.7,
+    )
+
+    # Style the markers
+    fig.update_traces(
+        marker={
+            "line": {"width": 1, "color": "rgba(255,255,255,0.3)"},
+        },
+    )
+
+    # Trendline styling
+    if trendline:
+        fig.update_traces(
+            selector={"mode": "lines"},
+            line={"color": "#FF9800", "dash": "dash", "width": 2},
+        )
+
+    fig.update_layout(
+        title=title,
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={
+            "gridcolor": "rgba(128,128,128,0.2)",
+            "title": x_title or x_column.replace("_", " ").title(),
+            "zeroline": False,
+        },
+        yaxis={
+            "gridcolor": "rgba(128,128,128,0.2)",
+            "title": y_title or y_column.replace("_", " ").title(),
+            "zeroline": False,
+        },
+        margin={"l": 10, "r": 10, "t": 40, "b": 10},
+        showlegend=color_column is not None,
+    )
+
+    return fig
+
+
+def create_bubble_chart(
+    data: list[dict[str, Any]],
+    x_column: str,
+    y_column: str,
+    size_column: str,
+    name_column: str | None = None,
+    color_column: str | None = None,
+    title: str | None = None,
+    x_title: str | None = None,
+    y_title: str | None = None,
+    size_max: int = 50,
+) -> go.Figure:
+    """Create bubble chart with sized markers.
+
+    Args:
+        data: List of data records.
+        x_column: Column name for x-axis values.
+        y_column: Column name for y-axis values.
+        size_column: Column name for bubble sizes.
+        name_column: Column name for labels.
+        color_column: Column name for colors.
+        title: Optional chart title.
+        x_title: X-axis title.
+        y_title: Y-axis title.
+        size_max: Maximum marker size in pixels.
+
+    Returns:
+        Plotly Figure object.
+    """
+    if not data:
+        return _create_empty_figure(title or "Bubble Chart")
+
+    df = pd.DataFrame(data)
+
+    fig = px.scatter(
+        df,
+        x=x_column,
+        y=y_column,
+        size=size_column,
+        color=color_column,
+        hover_name=name_column,
+        size_max=size_max,
+        opacity=0.7,
+    )
+
+    fig.update_layout(
+        title=title,
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={
+            "gridcolor": "rgba(128,128,128,0.2)",
+            "title": x_title or x_column.replace("_", " ").title(),
+        },
+        yaxis={
+            "gridcolor": "rgba(128,128,128,0.2)",
+            "title": y_title or y_column.replace("_", " ").title(),
+        },
+        margin={"l": 10, "r": 10, "t": 40, "b": 10},
+    )
+
+    return fig
+
+
+def _create_empty_figure(title: str) -> go.Figure:
+    """Create an empty figure with a message."""
+    fig = go.Figure()
+    fig.add_annotation(
+        text="No data available",
+        xref="paper",
+        yref="paper",
+        x=0.5,
+        y=0.5,
+        showarrow=False,
+        font={"size": 14, "color": "#888888"},
+    )
+    fig.update_layout(
+        title=title,
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={"visible": False},
+        yaxis={"visible": False},
+    )
+    return fig

portfolio_app/figures/toronto/summary_cards.py

@@ -0,0 +1,107 @@
+"""Summary card figure factories for KPI display."""
+
+from typing import Any
+
+import plotly.graph_objects as go
+
+
+def create_metric_card_figure(
+    value: float | int | str,
+    title: str,
+    delta: float | None = None,
+    delta_suffix: str = "%",
+    prefix: str = "",
+    suffix: str = "",
+    format_spec: str = ",.0f",
+    positive_is_good: bool = True,
+) -> go.Figure:
+    """Create a KPI indicator figure.
+
+    Args:
+        value: The main metric value.
+        title: Card title.
+        delta: Optional change value (for delta indicator).
+        delta_suffix: Suffix for delta value (e.g., '%').
+        prefix: Prefix for main value (e.g., '$').
+        suffix: Suffix for main value.
+        format_spec: Python format specification for the value.
+        positive_is_good: Whether positive delta is good (green) or bad (red).
+
+    Returns:
+        Plotly Figure object.
+    """
+    # Determine numeric value for indicator
+    if isinstance(value, int | float):
+        number_value: float | None = float(value)
+    else:
+        number_value = None
+
+    fig = go.Figure()
+
+    # Add indicator trace
+    indicator_config: dict[str, Any] = {
+        "mode": "number",
+        "value": number_value if number_value is not None else 0,
+        "title": {"text": title, "font": {"size": 14}},
+        "number": {
+            "font": {"size": 32},
+            "prefix": prefix,
+            "suffix": suffix,
+            "valueformat": format_spec,
+        },
+    }
+
+    # Add delta if provided
+    if delta is not None:
+        indicator_config["mode"] = "number+delta"
+        indicator_config["delta"] = {
+            "reference": number_value - delta if number_value else 0,
+            "relative": False,
+            "valueformat": ".1f",
+            "suffix": delta_suffix,
+            "increasing": {"color": "green" if positive_is_good else "red"},
+            "decreasing": {"color": "red" if positive_is_good else "green"},
+        }
+
+    fig.add_trace(go.Indicator(**indicator_config))
+
+    fig.update_layout(
+        height=120,
+        margin={"l": 20, "r": 20, "t": 40, "b": 20},
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font={"family": "Inter, sans-serif", "color": "#c9c9c9"},
+    )
+
+    return fig
+
+
+def create_summary_metrics(
+    metrics: dict[str, dict[str, Any]],
+) -> list[go.Figure]:
+    """Create multiple metric card figures.
+
+    Args:
+        metrics: Dictionary of metric configurations.
+            Key: metric name
+            Value: dict with 'value', 'title', 'delta' (optional), etc.
+
+    Returns:
+        List of Plotly Figure objects.
+    """
+    figures = []
+
+    for metric_config in metrics.values():
+        fig = create_metric_card_figure(
+            value=metric_config.get("value", 0),
+            title=metric_config.get("title", ""),
+            delta=metric_config.get("delta"),
+            delta_suffix=metric_config.get("delta_suffix", "%"),
+            prefix=metric_config.get("prefix", ""),
+            suffix=metric_config.get("suffix", ""),
+            format_spec=metric_config.get("format_spec", ",.0f"),
+            positive_is_good=metric_config.get("positive_is_good", True),
+        )
+        figures.append(fig)
+
+    return figures

portfolio_app/figures/toronto/time_series.py

@@ -0,0 +1,386 @@
+"""Time series figure factories for Toronto housing data."""
+
+from typing import Any
+
+import plotly.express as px
+import plotly.graph_objects as go
+
+
+def create_price_time_series(
+    data: list[dict[str, Any]],
+    date_column: str = "full_date",
+    price_column: str = "avg_price",
+    group_column: str | None = None,
+    title: str = "Average Price Over Time",
+    show_yoy: bool = True,
+) -> go.Figure:
+    """Create a time series chart for price data.
+
+    Args:
+        data: List of records with date and price columns.
+        date_column: Column name for dates.
+        price_column: Column name for price values.
+        group_column: Optional column for grouping (e.g., district_code).
+        title: Chart title.
+        show_yoy: Whether to show year-over-year change annotations.
+
+    Returns:
+        Plotly Figure object.
+    """
+    import pandas as pd
+
+    if not data:
+        fig = go.Figure()
+        fig.add_annotation(
+            text="No data available",
+            xref="paper",
+            yref="paper",
+            x=0.5,
+            y=0.5,
+            showarrow=False,
+            font={"color": "#888888"},
+        )
+        fig.update_layout(
+            title=title,
+            height=350,
+            paper_bgcolor="rgba(0,0,0,0)",
+            plot_bgcolor="rgba(0,0,0,0)",
+            font_color="#c9c9c9",
+        )
+        return fig
+
+    df = pd.DataFrame(data)
+    df[date_column] = pd.to_datetime(df[date_column])
+
+    if group_column and group_column in df.columns:
+        fig = px.line(
+            df,
+            x=date_column,
+            y=price_column,
+            color=group_column,
+            title=title,
+        )
+    else:
+        fig = px.line(
+            df,
+            x=date_column,
+            y=price_column,
+            title=title,
+        )
+
+    fig.update_layout(
+        height=350,
+        margin={"l": 40, "r": 20, "t": 50, "b": 40},
+        xaxis_title="Date",
+        yaxis_title=price_column.replace("_", " ").title(),
+        yaxis_tickprefix="$",
+        yaxis_tickformat=",",
+        hovermode="x unified",
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        yaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+    )
+
+    return fig
+
+
+def create_volume_time_series(
+    data: list[dict[str, Any]],
+    date_column: str = "full_date",
+    volume_column: str = "sales_count",
+    group_column: str | None = None,
+    title: str = "Sales Volume Over Time",
+    chart_type: str = "bar",
+) -> go.Figure:
+    """Create a time series chart for volume/count data.
+
+    Args:
+        data: List of records with date and volume columns.
+        date_column: Column name for dates.
+        volume_column: Column name for volume values.
+        group_column: Optional column for grouping.
+        title: Chart title.
+        chart_type: 'bar' or 'line'.
+
+    Returns:
+        Plotly Figure object.
+    """
+    import pandas as pd
+
+    if not data:
+        fig = go.Figure()
+        fig.add_annotation(
+            text="No data available",
+            xref="paper",
+            yref="paper",
+            x=0.5,
+            y=0.5,
+            showarrow=False,
+            font={"color": "#888888"},
+        )
+        fig.update_layout(
+            title=title,
+            height=350,
+            paper_bgcolor="rgba(0,0,0,0)",
+            plot_bgcolor="rgba(0,0,0,0)",
+            font_color="#c9c9c9",
+        )
+        return fig
+
+    df = pd.DataFrame(data)
+    df[date_column] = pd.to_datetime(df[date_column])
+
+    if chart_type == "bar":
+        if group_column and group_column in df.columns:
+            fig = px.bar(
+                df,
+                x=date_column,
+                y=volume_column,
+                color=group_column,
+                title=title,
+            )
+        else:
+            fig = px.bar(
+                df,
+                x=date_column,
+                y=volume_column,
+                title=title,
+            )
+    else:
+        if group_column and group_column in df.columns:
+            fig = px.line(
+                df,
+                x=date_column,
+                y=volume_column,
+                color=group_column,
+                title=title,
+            )
+        else:
+            fig = px.line(
+                df,
+                x=date_column,
+                y=volume_column,
+                title=title,
+            )
+
+    fig.update_layout(
+        height=350,
+        margin={"l": 40, "r": 20, "t": 50, "b": 40},
+        xaxis_title="Date",
+        yaxis_title=volume_column.replace("_", " ").title(),
+        yaxis_tickformat=",",
+        hovermode="x unified",
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        yaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+    )
+
+    return fig
+
+
+def create_market_comparison_chart(
+    data: list[dict[str, Any]],
+    date_column: str = "full_date",
+    metrics: list[str] | None = None,
+    title: str = "Market Indicators",
+) -> go.Figure:
+    """Create a multi-metric comparison chart.
+
+    Args:
+        data: List of records with date and metric columns.
+        date_column: Column name for dates.
+        metrics: List of metric columns to display.
+        title: Chart title.
+
+    Returns:
+        Plotly Figure object with secondary y-axis.
+    """
+    import pandas as pd
+    from plotly.subplots import make_subplots
+
+    if not data:
+        fig = go.Figure()
+        fig.add_annotation(
+            text="No data available",
+            xref="paper",
+            yref="paper",
+            x=0.5,
+            y=0.5,
+            showarrow=False,
+            font={"color": "#888888"},
+        )
+        fig.update_layout(
+            title=title,
+            height=400,
+            paper_bgcolor="rgba(0,0,0,0)",
+            plot_bgcolor="rgba(0,0,0,0)",
+            font_color="#c9c9c9",
+        )
+        return fig
+
+    if metrics is None:
+        metrics = ["avg_price", "sales_count"]
+
+    df = pd.DataFrame(data)
+    df[date_column] = pd.to_datetime(df[date_column])
+
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+
+    colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728"]
+
+    for i, metric in enumerate(metrics[:4]):
+        if metric not in df.columns:
+            continue
+
+        secondary = i > 0
+        fig.add_trace(
+            go.Scatter(
+                x=df[date_column],
+                y=df[metric],
+                name=metric.replace("_", " ").title(),
+                line={"color": colors[i % len(colors)]},
+            ),
+            secondary_y=secondary,
+        )
+
+    fig.update_layout(
+        title=title,
+        height=400,
+        margin={"l": 40, "r": 40, "t": 50, "b": 40},
+        hovermode="x unified",
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        yaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        legend={
+            "orientation": "h",
+            "yanchor": "bottom",
+            "y": 1.02,
+            "xanchor": "right",
+            "x": 1,
+            "font": {"color": "#c9c9c9"},
+        },
+    )
+
+    return fig
+
+
+def add_policy_markers(
+    fig: go.Figure,
+    policy_events: list[dict[str, Any]],
+    date_column: str = "event_date",
+    y_position: float | None = None,
+) -> go.Figure:
+    """Add policy event markers to an existing time series figure.
+
+    Args:
+        fig: Existing Plotly figure to add markers to.
+        policy_events: List of policy event dicts with date and metadata.
+        date_column: Column name for event dates.
+        y_position: Y position for markers. If None, uses top of chart.
+
+    Returns:
+        Updated Plotly Figure object with policy markers.
+    """
+    if not policy_events:
+        return fig
+
+    # Color mapping for policy categories
+    category_colors = {
+        "monetary": "#1f77b4",  # Blue
+        "tax": "#2ca02c",  # Green
+        "regulatory": "#ff7f0e",  # Orange
+        "supply": "#9467bd",  # Purple
+        "economic": "#d62728",  # Red
+    }
+
+    # Symbol mapping for expected direction
+    direction_symbols = {
+        "bullish": "triangle-up",
+        "bearish": "triangle-down",
+        "neutral": "circle",
+    }
+
+    for event in policy_events:
+        event_date = event.get(date_column)
+        category = event.get("category", "economic")
+        direction = event.get("expected_direction", "neutral")
+        title = event.get("title", "Policy Event")
+        level = event.get("level", "federal")
+
+        color = category_colors.get(category, "#666666")
+        symbol = direction_symbols.get(direction, "circle")
+
+        # Add vertical line for the event
+        fig.add_vline(
+            x=event_date,
+            line_dash="dot",
+            line_color=color,
+            opacity=0.5,
+            annotation_text="",
+        )
+
+        # Add marker with hover info
+        fig.add_trace(
+            go.Scatter(
+                x=[event_date],
+                y=[y_position] if y_position else [None],  # type: ignore[list-item]
+                mode="markers",
+                marker={
+                    "symbol": symbol,
+                    "size": 12,
+                    "color": color,
+                    "line": {"width": 1, "color": "white"},
+                },
+                name=title,
+                hovertemplate=(
+                    f"<b>{title}</b><br>"
+                    f"Date: %{{x}}<br>"
+                    f"Level: {level.title()}<br>"
+                    f"Category: {category.title()}<br>"
+                    f"<extra></extra>"
+                ),
+                showlegend=False,
+            )
+        )
+
+    return fig
+
+
+def create_time_series_with_events(
+    data: list[dict[str, Any]],
+    policy_events: list[dict[str, Any]],
+    date_column: str = "full_date",
+    value_column: str = "avg_price",
+    title: str = "Price Trend with Policy Events",
+) -> go.Figure:
+    """Create a time series chart with policy event markers.
+
+    Args:
+        data: Time series data.
+        policy_events: Policy events to overlay.
+        date_column: Column name for dates.
+        value_column: Column name for values.
+        title: Chart title.
+
+    Returns:
+        Plotly Figure with time series and policy markers.
+    """
+    # Create base time series
+    fig = create_price_time_series(
+        data=data,
+        date_column=date_column,
+        price_column=value_column,
+        title=title,
+    )
+
+    # Add policy markers at the top of the chart
+    if policy_events:
+        fig = add_policy_markers(fig, policy_events)
+
+    return fig