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/facts.py

@@ -0,0 +1,69 @@
+"""SQLAlchemy models for fact tables."""
+
+from sqlalchemy import ForeignKey, Integer, Numeric, String
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from .base import Base
+
+
+class FactPurchases(Base):
+    """Fact table for TRREB purchase/sales data.
+
+    Grain: One row per district per month.
+    """
+
+    __tablename__ = "fact_purchases"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    date_key: Mapped[int] = mapped_column(
+        Integer, ForeignKey("dim_time.date_key"), nullable=False
+    )
+    district_key: Mapped[int] = mapped_column(
+        Integer, ForeignKey("dim_trreb_district.district_key"), nullable=False
+    )
+    sales_count: Mapped[int] = mapped_column(Integer, nullable=False)
+    dollar_volume: Mapped[float] = mapped_column(Numeric(15, 2), nullable=False)
+    avg_price: Mapped[float] = mapped_column(Numeric(12, 2), nullable=False)
+    median_price: Mapped[float] = mapped_column(Numeric(12, 2), nullable=False)
+    new_listings: Mapped[int] = mapped_column(Integer, nullable=False)
+    active_listings: Mapped[int] = mapped_column(Integer, nullable=False)
+    avg_dom: Mapped[int] = mapped_column(Integer, nullable=False)  # Days on market
+    avg_sp_lp: Mapped[float] = mapped_column(
+        Numeric(5, 2), nullable=False
+    )  # Sale/List ratio
+
+    # Relationships
+    time = relationship("DimTime", backref="purchases")
+    district = relationship("DimTRREBDistrict", backref="purchases")
+
+
+class FactRentals(Base):
+    """Fact table for CMHC rental market data.
+
+    Grain: One row per zone per bedroom type per survey year.
+    """
+
+    __tablename__ = "fact_rentals"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    date_key: Mapped[int] = mapped_column(
+        Integer, ForeignKey("dim_time.date_key"), nullable=False
+    )
+    zone_key: Mapped[int] = mapped_column(
+        Integer, ForeignKey("dim_cmhc_zone.zone_key"), nullable=False
+    )
+    bedroom_type: Mapped[str] = mapped_column(String(20), nullable=False)
+    universe: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    avg_rent: Mapped[float | None] = mapped_column(Numeric(10, 2), nullable=True)
+    median_rent: Mapped[float | None] = mapped_column(Numeric(10, 2), nullable=True)
+    vacancy_rate: Mapped[float | None] = mapped_column(Numeric(5, 2), nullable=True)
+    availability_rate: Mapped[float | None] = mapped_column(
+        Numeric(5, 2), nullable=True
+    )
+    turnover_rate: Mapped[float | None] = mapped_column(Numeric(5, 2), nullable=True)
+    rent_change_pct: Mapped[float | None] = mapped_column(Numeric(5, 2), nullable=True)
+    reliability_code: Mapped[str | None] = mapped_column(String(2), nullable=True)
+
+    # Relationships
+    time = relationship("DimTime", backref="rentals")
+    zone = relationship("DimCMHCZone", backref="rentals")