chore: Delete legacy TRREB dbt models

- Delete stg_trreb__purchases.sql and stg_dimensions__trreb_districts.sql
- Delete int_purchases__monthly.sql
- Delete mart_toronto_purchases.sql and mart_toronto_market_summary.sql
- Update _sources.yml to remove fact_purchases and dim_trreb_district
- Update _staging.yml to remove TRREB staging models
- Update _intermediate.yml to remove int_purchases__monthly
- Update _marts.yml to remove purchase-related marts

Closes #48

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

dbt/models/marts/_marts.yml

@@ -1,15 +1,6 @@
 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:
@@ -18,6 +9,3 @@ models:
         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

@@ -1,81 +0,0 @@
--- 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

@@ -1,79 +0,0 @@
--- 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