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/trreb.py

@@ -0,0 +1,129 @@
+"""Loader for TRREB purchase data into fact_purchases."""
+
+from sqlalchemy.orm import Session
+
+from portfolio_app.toronto.models import DimTime, DimTRREBDistrict, FactPurchases
+from portfolio_app.toronto.schemas import TRREBMonthlyRecord, TRREBMonthlyReport
+
+from .base import get_session, upsert_by_key
+from .dimensions import generate_date_key
+
+
+def load_trreb_purchases(
+    report: TRREBMonthlyReport,
+    session: Session | None = None,
+) -> int:
+    """Load TRREB monthly report data into fact_purchases.
+
+    Args:
+        report: Validated TRREB monthly report containing records.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded.
+    """
+
+    def _load(sess: Session) -> int:
+        # Get district key mapping
+        districts = sess.query(DimTRREBDistrict).all()
+        district_map = {d.district_code: d.district_key for d in districts}
+
+        # Build date key from report date
+        date_key = generate_date_key(report.report_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 report.records:
+            district_key = district_map.get(record.area_code)
+            if not district_key:
+                # Skip records for unknown districts (e.g., aggregate rows)
+                continue
+
+            fact = FactPurchases(
+                date_key=date_key,
+                district_key=district_key,
+                sales_count=record.sales,
+                dollar_volume=record.dollar_volume,
+                avg_price=record.avg_price,
+                median_price=record.median_price,
+                new_listings=record.new_listings,
+                active_listings=record.active_listings,
+                avg_dom=record.avg_dom,
+                avg_sp_lp=record.avg_sp_lp,
+            )
+            records.append(fact)
+
+        inserted, updated = upsert_by_key(
+            sess, FactPurchases, records, ["date_key", "district_key"]
+        )
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)
+
+
+def load_trreb_record(
+    record: TRREBMonthlyRecord,
+    session: Session | None = None,
+) -> int:
+    """Load a single TRREB record into fact_purchases.
+
+    Args:
+        record: Single validated TRREB monthly record.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded (0 or 1).
+    """
+
+    def _load(sess: Session) -> int:
+        # Get district key
+        district = (
+            sess.query(DimTRREBDistrict)
+            .filter_by(district_code=record.area_code)
+            .first()
+        )
+        if not district:
+            return 0
+
+        date_key = generate_date_key(record.report_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 = FactPurchases(
+            date_key=date_key,
+            district_key=district.district_key,
+            sales_count=record.sales,
+            dollar_volume=record.dollar_volume,
+            avg_price=record.avg_price,
+            median_price=record.median_price,
+            new_listings=record.new_listings,
+            active_listings=record.active_listings,
+            avg_dom=record.avg_dom,
+            avg_sp_lp=record.avg_sp_lp,
+        )
+
+        inserted, updated = upsert_by_key(
+            sess, FactPurchases, [fact], ["date_key", "district_key"]
+        )
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)