feat: add Pydantic schemas, SQLAlchemy models, and parser structure

Sprint 3 implementation:
- Pydantic schemas for TRREB, CMHC, and dimension data validation
- SQLAlchemy models with PostGIS geometry for fact and dimension tables
- Parser structure (stubs) for TRREB PDF and CMHC CSV processing

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

portfolio_app/toronto/models/dimensions.py

@@ -0,0 +1,104 @@
+"""SQLAlchemy models for dimension tables."""
+
+from datetime import date
+
+from geoalchemy2 import Geometry
+from sqlalchemy import Boolean, Date, Integer, Numeric, String, Text
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class DimTime(Base):
+    """Time dimension table."""
+
+    __tablename__ = "dim_time"
+
+    date_key: Mapped[int] = mapped_column(Integer, primary_key=True)
+    full_date: Mapped[date] = mapped_column(Date, nullable=False, unique=True)
+    year: Mapped[int] = mapped_column(Integer, nullable=False)
+    month: Mapped[int] = mapped_column(Integer, nullable=False)
+    quarter: Mapped[int] = mapped_column(Integer, nullable=False)
+    month_name: Mapped[str] = mapped_column(String(20), nullable=False)
+    is_month_start: Mapped[bool] = mapped_column(Boolean, default=True)
+
+
+class DimTRREBDistrict(Base):
+    """TRREB district dimension table with PostGIS geometry."""
+
+    __tablename__ = "dim_trreb_district"
+
+    district_key: Mapped[int] = mapped_column(
+        Integer, primary_key=True, autoincrement=True
+    )
+    district_code: Mapped[str] = mapped_column(String(3), nullable=False, unique=True)
+    district_name: Mapped[str] = mapped_column(String(100), nullable=False)
+    area_type: Mapped[str] = mapped_column(String(10), nullable=False)
+    geometry = mapped_column(Geometry("POLYGON", srid=4326), nullable=True)
+
+
+class DimCMHCZone(Base):
+    """CMHC zone dimension table with PostGIS geometry."""
+
+    __tablename__ = "dim_cmhc_zone"
+
+    zone_key: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    zone_code: Mapped[str] = mapped_column(String(10), nullable=False, unique=True)
+    zone_name: Mapped[str] = mapped_column(String(100), nullable=False)
+    geometry = mapped_column(Geometry("POLYGON", srid=4326), nullable=True)
+
+
+class DimNeighbourhood(Base):
+    """City of Toronto neighbourhood dimension.
+
+    Note: No FK to fact tables in V1 - reference overlay only.
+    """
+
+    __tablename__ = "dim_neighbourhood"
+
+    neighbourhood_id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    name: Mapped[str] = mapped_column(String(100), nullable=False)
+    geometry = mapped_column(Geometry("POLYGON", srid=4326), nullable=True)
+    population: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    land_area_sqkm: Mapped[float | None] = mapped_column(Numeric(10, 4), nullable=True)
+    pop_density_per_sqkm: Mapped[float | None] = mapped_column(
+        Numeric(10, 2), nullable=True
+    )
+    pct_bachelors_or_higher: Mapped[float | None] = mapped_column(
+        Numeric(5, 2), nullable=True
+    )
+    median_household_income: Mapped[float | None] = mapped_column(
+        Numeric(12, 2), nullable=True
+    )
+    pct_owner_occupied: Mapped[float | None] = mapped_column(
+        Numeric(5, 2), nullable=True
+    )
+    pct_renter_occupied: Mapped[float | None] = mapped_column(
+        Numeric(5, 2), nullable=True
+    )
+    census_year: Mapped[int] = mapped_column(Integer, default=2021)
+
+
+class DimPolicyEvent(Base):
+    """Policy event dimension for time-series annotation."""
+
+    __tablename__ = "dim_policy_event"
+
+    event_id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    event_date: Mapped[date] = mapped_column(Date, nullable=False)
+    effective_date: Mapped[date | None] = mapped_column(Date, nullable=True)
+    level: Mapped[str] = mapped_column(
+        String(20), nullable=False
+    )  # federal/provincial/municipal
+    category: Mapped[str] = mapped_column(
+        String(20), nullable=False
+    )  # monetary/tax/regulatory/supply/economic
+    title: Mapped[str] = mapped_column(String(200), nullable=False)
+    description: Mapped[str | None] = mapped_column(Text, nullable=True)
+    expected_direction: Mapped[str] = mapped_column(
+        String(10), nullable=False
+    )  # bearish/bullish/neutral
+    source_url: Mapped[str | None] = mapped_column(String(500), nullable=True)
+    confidence: Mapped[str] = mapped_column(
+        String(10), default="medium"
+    )  # high/medium/low