feat: add loaders and dbt models for Toronto housing data

Sprint 4 implementation:

Loaders:
- base.py: Session management, bulk insert, upsert utilities
- dimensions.py: Load time, district, zone, neighbourhood, policy dimensions
- trreb.py: Load TRREB purchase data to fact_purchases
- cmhc.py: Load CMHC rental data to fact_rentals

dbt Project:
- Project configuration (dbt_project.yml, packages.yml)
- Staging models for all fact and dimension tables
- Intermediate models with dimension enrichment
- Marts: purchase analysis, rental analysis, market summary

Closes #16

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

portfolio_app/toronto/loaders/cmhc.py

@@ -0,0 +1,137 @@
+"""Loader for CMHC rental data into fact_rentals."""
+
+from sqlalchemy.orm import Session
+
+from portfolio_app.toronto.models import DimCMHCZone, DimTime, FactRentals
+from portfolio_app.toronto.schemas import CMHCAnnualSurvey, CMHCRentalRecord
+
+from .base import get_session, upsert_by_key
+from .dimensions import generate_date_key
+
+
+def load_cmhc_rentals(
+    survey: CMHCAnnualSurvey,
+    session: Session | None = None,
+) -> int:
+    """Load CMHC annual survey data into fact_rentals.
+
+    Args:
+        survey: Validated CMHC annual survey containing records.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded.
+    """
+    from datetime import date
+
+    def _load(sess: Session) -> int:
+        # Get zone key mapping
+        zones = sess.query(DimCMHCZone).all()
+        zone_map = {z.zone_code: z.zone_key for z in zones}
+
+        # CMHC surveys are annual - use October 1st as reference date
+        survey_date = date(survey.survey_year, 10, 1)
+        date_key = generate_date_key(survey_date)
+
+        # Verify time dimension exists
+        time_dim = sess.query(DimTime).filter_by(date_key=date_key).first()
+        if not time_dim:
+            raise ValueError(
+                f"Time dimension not found for date_key {date_key}. "
+                "Load time dimension first."
+            )
+
+        records = []
+        for record in survey.records:
+            zone_key = zone_map.get(record.zone_code)
+            if not zone_key:
+                # Skip records for unknown zones
+                continue
+
+            fact = FactRentals(
+                date_key=date_key,
+                zone_key=zone_key,
+                bedroom_type=record.bedroom_type.value,
+                universe=record.universe,
+                avg_rent=record.average_rent,
+                median_rent=record.median_rent,
+                vacancy_rate=record.vacancy_rate,
+                availability_rate=record.availability_rate,
+                turnover_rate=record.turnover_rate,
+                rent_change_pct=record.rent_change_pct,
+                reliability_code=record.average_rent_reliability.value
+                if record.average_rent_reliability
+                else None,
+            )
+            records.append(fact)
+
+        inserted, updated = upsert_by_key(
+            sess, FactRentals, records, ["date_key", "zone_key", "bedroom_type"]
+        )
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)
+
+
+def load_cmhc_record(
+    record: CMHCRentalRecord,
+    survey_year: int,
+    session: Session | None = None,
+) -> int:
+    """Load a single CMHC record into fact_rentals.
+
+    Args:
+        record: Single validated CMHC rental record.
+        survey_year: Year of the survey.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded (0 or 1).
+    """
+    from datetime import date
+
+    def _load(sess: Session) -> int:
+        # Get zone key
+        zone = sess.query(DimCMHCZone).filter_by(zone_code=record.zone_code).first()
+        if not zone:
+            return 0
+
+        survey_date = date(survey_year, 10, 1)
+        date_key = generate_date_key(survey_date)
+
+        # Verify time dimension exists
+        time_dim = sess.query(DimTime).filter_by(date_key=date_key).first()
+        if not time_dim:
+            raise ValueError(
+                f"Time dimension not found for date_key {date_key}. "
+                "Load time dimension first."
+            )
+
+        fact = FactRentals(
+            date_key=date_key,
+            zone_key=zone.zone_key,
+            bedroom_type=record.bedroom_type.value,
+            universe=record.universe,
+            avg_rent=record.average_rent,
+            median_rent=record.median_rent,
+            vacancy_rate=record.vacancy_rate,
+            availability_rate=record.availability_rate,
+            turnover_rate=record.turnover_rate,
+            rent_change_pct=record.rent_change_pct,
+            reliability_code=record.average_rent_reliability.value
+            if record.average_rent_reliability
+            else None,
+        )
+
+        inserted, updated = upsert_by_key(
+            sess, FactRentals, [fact], ["date_key", "zone_key", "bedroom_type"]
+        )
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)