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>

dbt/models/intermediate/_intermediate.yml

@@ -0,0 +1,24 @@
+version: 2
+
+models:
+  - name: int_purchases__monthly
+    description: "Purchase data enriched with time and district dimensions"
+    columns:
+      - name: purchase_id
+        tests:
+          - unique
+          - not_null
+      - name: district_code
+        tests:
+          - not_null
+
+  - name: int_rentals__annual
+    description: "Rental data enriched with time and zone dimensions"
+    columns:
+      - name: rental_id
+        tests:
+          - unique
+          - not_null
+      - name: zone_code
+        tests:
+          - not_null

dbt/models/intermediate/int_purchases__monthly.sql

@@ -0,0 +1,62 @@
+-- Intermediate: Monthly purchase data enriched with dimensions
+-- Joins purchases with time and district dimensions for analysis
+
+with purchases as (
+    select * from {{ ref('stg_trreb__purchases') }}
+),
+
+time_dim as (
+    select * from {{ ref('stg_dimensions__time') }}
+),
+
+district_dim as (
+    select * from {{ ref('stg_dimensions__trreb_districts') }}
+),
+
+enriched as (
+    select
+        p.purchase_id,
+
+        -- Time attributes
+        t.date_key,
+        t.full_date,
+        t.year,
+        t.month,
+        t.quarter,
+        t.month_name,
+
+        -- District attributes
+        d.district_key,
+        d.district_code,
+        d.district_name,
+        d.area_type,
+
+        -- Metrics
+        p.sales_count,
+        p.dollar_volume,
+        p.avg_price,
+        p.median_price,
+        p.new_listings,
+        p.active_listings,
+        p.days_on_market,
+        p.sale_to_list_ratio,
+
+        -- Calculated metrics
+        case
+            when p.active_listings > 0
+            then round(p.sales_count::numeric / p.active_listings, 3)
+            else null
+        end as absorption_rate,
+
+        case
+            when p.sales_count > 0
+            then round(p.active_listings::numeric / p.sales_count, 1)
+            else null
+        end as months_of_inventory
+
+    from purchases p
+    inner join time_dim t on p.date_key = t.date_key
+    inner join district_dim d on p.district_key = d.district_key
+)
+
+select * from enriched

dbt/models/intermediate/int_rentals__annual.sql

@@ -0,0 +1,57 @@
+-- Intermediate: Annual rental data enriched with dimensions
+-- Joins rentals with time and zone dimensions for analysis
+
+with rentals as (
+    select * from {{ ref('stg_cmhc__rentals') }}
+),
+
+time_dim as (
+    select * from {{ ref('stg_dimensions__time') }}
+),
+
+zone_dim as (
+    select * from {{ ref('stg_dimensions__cmhc_zones') }}
+),
+
+enriched as (
+    select
+        r.rental_id,
+
+        -- Time attributes
+        t.date_key,
+        t.full_date,
+        t.year,
+        t.month,
+        t.quarter,
+
+        -- Zone attributes
+        z.zone_key,
+        z.zone_code,
+        z.zone_name,
+
+        -- Bedroom type
+        r.bedroom_type,
+
+        -- Metrics
+        r.rental_universe,
+        r.avg_rent,
+        r.median_rent,
+        r.vacancy_rate,
+        r.availability_rate,
+        r.turnover_rate,
+        r.year_over_year_rent_change,
+        r.reliability_code,
+
+        -- Calculated metrics
+        case
+            when r.rental_universe > 0 and r.vacancy_rate is not null
+            then round(r.rental_universe * (r.vacancy_rate / 100), 0)
+            else null
+        end as vacant_units_estimate
+
+    from rentals r
+    inner join time_dim t on r.date_key = t.date_key
+    inner join zone_dim z on r.zone_key = z.zone_key
+)
+
+select * from enriched