Files
personal-portfolio/dbt/models/marts/mart_toronto_purchases.sql
lmiranda 457bb49395 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>
2026-01-11 16:07:30 -05:00

80 lines
1.9 KiB
SQL

-- 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