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/marts/_marts.yml

@@ -0,0 +1,23 @@
+version: 2
+
+models:
+  - name: mart_toronto_purchases
+    description: "Final mart for Toronto purchase/sales analysis by district and time"
+    columns:
+      - name: purchase_id
+        description: "Unique purchase record identifier"
+        tests:
+          - unique
+          - not_null
+
+  - name: mart_toronto_rentals
+    description: "Final mart for Toronto rental market analysis by zone and time"
+    columns:
+      - name: rental_id
+        description: "Unique rental record identifier"
+        tests:
+          - unique
+          - not_null
+
+  - name: mart_toronto_market_summary
+    description: "Combined market summary aggregating purchases and rentals at Toronto level"

dbt/models/marts/mart_toronto_market_summary.sql

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+-- Mart: Toronto Market Summary
+-- Aggregated view combining purchase and rental market indicators
+-- Grain: One row per year-month
+
+with purchases_agg as (
+    select
+        year,
+        month,
+        month_name,
+        quarter,
+
+        -- Aggregate purchase metrics across all districts
+        sum(sales_count) as total_sales,
+        sum(dollar_volume) as total_dollar_volume,
+        round(avg(avg_price), 0) as avg_price_all_districts,
+        round(avg(median_price), 0) as median_price_all_districts,
+        sum(new_listings) as total_new_listings,
+        sum(active_listings) as total_active_listings,
+        round(avg(days_on_market), 0) as avg_days_on_market,
+        round(avg(sale_to_list_ratio), 2) as avg_sale_to_list_ratio,
+        round(avg(absorption_rate), 3) as avg_absorption_rate,
+        round(avg(months_of_inventory), 1) as avg_months_of_inventory,
+        round(avg(avg_price_yoy_pct), 2) as avg_price_yoy_pct
+
+    from {{ ref('mart_toronto_purchases') }}
+    group by year, month, month_name, quarter
+),
+
+rentals_agg as (
+    select
+        year,
+
+        -- Aggregate rental metrics across all zones (all bedroom types)
+        round(avg(avg_rent), 0) as avg_rent_all_zones,
+        round(avg(vacancy_rate), 2) as avg_vacancy_rate,
+        round(avg(rent_change_pct), 2) as avg_rent_change_pct,
+        sum(rental_universe) as total_rental_universe
+
+    from {{ ref('mart_toronto_rentals') }}
+    group by year
+),
+
+final as (
+    select
+        p.year,
+        p.month,
+        p.month_name,
+        p.quarter,
+
+        -- Purchase market indicators
+        p.total_sales,
+        p.total_dollar_volume,
+        p.avg_price_all_districts,
+        p.median_price_all_districts,
+        p.total_new_listings,
+        p.total_active_listings,
+        p.avg_days_on_market,
+        p.avg_sale_to_list_ratio,
+        p.avg_absorption_rate,
+        p.avg_months_of_inventory,
+        p.avg_price_yoy_pct,
+
+        -- Rental market indicators (annual, so join on year)
+        r.avg_rent_all_zones,
+        r.avg_vacancy_rate,
+        r.avg_rent_change_pct,
+        r.total_rental_universe,
+
+        -- Affordability indicator (price to rent ratio)
+        case
+            when r.avg_rent_all_zones > 0
+            then round(p.avg_price_all_districts / (r.avg_rent_all_zones * 12), 1)
+            else null
+        end as price_to_annual_rent_ratio
+
+    from purchases_agg p
+    left join rentals_agg r on p.year = r.year
+)
+
+select * from final
+order by year desc, month desc

dbt/models/marts/mart_toronto_purchases.sql

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+-- Mart: Toronto Purchase Market Analysis
+-- Final analytical table for purchase/sales data visualization
+-- Grain: One row per district per month
+
+with purchases as (
+    select * from {{ ref('int_purchases__monthly') }}
+),
+
+-- Add year-over-year calculations
+with_yoy as (
+    select
+        p.*,
+
+        -- Previous year same month values
+        lag(p.avg_price, 12) over (
+            partition by p.district_code
+            order by p.date_key
+        ) as avg_price_prev_year,
+
+        lag(p.sales_count, 12) over (
+            partition by p.district_code
+            order by p.date_key
+        ) as sales_count_prev_year,
+
+        lag(p.median_price, 12) over (
+            partition by p.district_code
+            order by p.date_key
+        ) as median_price_prev_year
+
+    from purchases p
+),
+
+final as (
+    select
+        purchase_id,
+        date_key,
+        full_date,
+        year,
+        month,
+        quarter,
+        month_name,
+        district_key,
+        district_code,
+        district_name,
+        area_type,
+        sales_count,
+        dollar_volume,
+        avg_price,
+        median_price,
+        new_listings,
+        active_listings,
+        days_on_market,
+        sale_to_list_ratio,
+        absorption_rate,
+        months_of_inventory,
+
+        -- Year-over-year changes
+        case
+            when avg_price_prev_year > 0
+            then round(((avg_price - avg_price_prev_year) / avg_price_prev_year) * 100, 2)
+            else null
+        end as avg_price_yoy_pct,
+
+        case
+            when sales_count_prev_year > 0
+            then round(((sales_count - sales_count_prev_year)::numeric / sales_count_prev_year) * 100, 2)
+            else null
+        end as sales_count_yoy_pct,
+
+        case
+            when median_price_prev_year > 0
+            then round(((median_price - median_price_prev_year) / median_price_prev_year) * 100, 2)
+            else null
+        end as median_price_yoy_pct
+
+    from with_yoy
+)
+
+select * from final

dbt/models/marts/mart_toronto_rentals.sql

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+-- Mart: Toronto Rental Market Analysis
+-- Final analytical table for rental market visualization
+-- Grain: One row per zone per bedroom type per survey year
+
+with rentals as (
+    select * from {{ ref('int_rentals__annual') }}
+),
+
+-- Add year-over-year calculations
+with_yoy as (
+    select
+        r.*,
+
+        -- Previous year values
+        lag(r.avg_rent, 1) over (
+            partition by r.zone_code, r.bedroom_type
+            order by r.year
+        ) as avg_rent_prev_year,
+
+        lag(r.vacancy_rate, 1) over (
+            partition by r.zone_code, r.bedroom_type
+            order by r.year
+        ) as vacancy_rate_prev_year
+
+    from rentals r
+),
+
+final as (
+    select
+        rental_id,
+        date_key,
+        full_date,
+        year,
+        quarter,
+        zone_key,
+        zone_code,
+        zone_name,
+        bedroom_type,
+        rental_universe,
+        avg_rent,
+        median_rent,
+        vacancy_rate,
+        availability_rate,
+        turnover_rate,
+        year_over_year_rent_change,
+        reliability_code,
+        vacant_units_estimate,
+
+        -- Calculated year-over-year (if not provided)
+        coalesce(
+            year_over_year_rent_change,
+            case
+                when avg_rent_prev_year > 0
+                then round(((avg_rent - avg_rent_prev_year) / avg_rent_prev_year) * 100, 2)
+                else null
+            end
+        ) as rent_change_pct,
+
+        vacancy_rate - vacancy_rate_prev_year as vacancy_rate_change
+
+    from with_yoy
+)
+
+select * from final