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chore: Delete legacy TRREB dbt models

Browse Source 
- 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>
This commit is contained in:
Leo Miranda
2026-01-16 10:12:58 -05:00
parent fcaefabce8
commit f5f2bf3706
9 changed files with 2 additions and 340 deletions
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11
dbt/models/intermediate/_intermediate.yml
View File

@@ -1,17 +1,6 @@
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:

62
dbt/models/intermediate/int_purchases__monthly.sql
View File

@@ -1,62 +0,0 @@
-- 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

12
dbt/models/marts/_marts.yml
View File

@@ -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"

81
dbt/models/marts/mart_toronto_market_summary.sql
View File

@@ -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

79
dbt/models/marts/mart_toronto_purchases.sql
View File

@@ -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

22
dbt/models/staging/_sources.yml
View File

@@ -2,20 +2,10 @@ version: 2
sources:
- name: toronto_housing
description: "Toronto housing data loaded from TRREB and CMHC sources"
description: "Toronto housing data loaded from CMHC and City of Toronto sources"
database: portfolio
schema: public
tables:
- name: fact_purchases
description: "TRREB monthly purchase/sales statistics by district"
columns:
- name: id
description: "Primary key"
- name: date_key
description: "Foreign key to dim_time"
- name: district_key
description: "Foreign key to dim_trreb_district"
- name: fact_rentals
description: "CMHC annual rental survey data by zone and bedroom type"
columns:
@@ -32,14 +22,6 @@ sources:
- name: date_key
description: "Primary key (YYYYMMDD format)"
- name: dim_trreb_district
description: "TRREB district dimension with geometry"
columns:
- name: district_key
description: "Primary key"
- name: district_code
description: "TRREB district code"
- name: dim_cmhc_zone
description: "CMHC zone dimension with geometry"
columns:
@@ -49,7 +31,7 @@ sources:
description: "CMHC zone code"
- name: dim_neighbourhood
description: "City of Toronto neighbourhoods (reference only)"
description: "City of Toronto neighbourhoods (158 official boundaries)"
columns:
- name: neighbourhood_id
description: "Primary key"

31
dbt/models/staging/_staging.yml
View File

@@ -1,23 +1,6 @@
version: 2
models:
- name: stg_trreb__purchases
description: "Staged TRREB purchase/sales data from fact_purchases"
columns:
- name: purchase_id
description: "Unique identifier for purchase record"
tests:
- unique
- not_null
- name: date_key
description: "Date dimension key (YYYYMMDD)"
tests:
- not_null
- name: district_key
description: "TRREB district dimension key"
tests:
- not_null
- name: stg_cmhc__rentals
description: "Staged CMHC rental market data from fact_rentals"
columns:
@@ -44,20 +27,6 @@ models:
- unique
- not_null
- name: stg_dimensions__trreb_districts
description: "Staged TRREB district dimension"
columns:
- name: district_key
description: "District dimension key"
tests:
- unique
- not_null
- name: district_code
description: "TRREB district code (e.g., W01, C01)"
tests:
- unique
- not_null
- name: stg_dimensions__cmhc_zones
description: "Staged CMHC zone dimension"
columns:

19
dbt/models/staging/stg_dimensions__trreb_districts.sql
View File

@@ -1,19 +0,0 @@
-- Staged TRREB district dimension
-- Source: dim_trreb_district table
-- Grain: One row per district
with source as (
select * from {{ source('toronto_housing', 'dim_trreb_district') }}
),
staged as (
select
district_key,
district_code,
district_name,
area_type,
geometry
from source
)
select * from staged

25
dbt/models/staging/stg_trreb__purchases.sql
View File

@@ -1,25 +0,0 @@
-- Staged TRREB purchase/sales data
-- Source: fact_purchases table loaded from TRREB Market Watch PDFs
-- Grain: One row per district per month
with source as (
select * from {{ source('toronto_housing', 'fact_purchases') }}
),
staged as (
select
id as purchase_id,
date_key,
district_key,
sales_count,
dollar_volume,
avg_price,
median_price,
new_listings,
active_listings,
avg_dom as days_on_market,
avg_sp_lp as sale_to_list_ratio
from source
)
select * from staged