lmiranda
457bb49395
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>
65 lines
1.5 KiB
SQL
65 lines
1.5 KiB
SQL
-- Mart: Toronto Rental Market Analysis
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-- Final analytical table for rental market visualization
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-- Grain: One row per zone per bedroom type per survey year
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with rentals as (
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select * from {{ ref('int_rentals__annual') }}
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),
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-- Add year-over-year calculations
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with_yoy as (
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select
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r.*,
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-- Previous year values
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lag(r.avg_rent, 1) over (
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partition by r.zone_code, r.bedroom_type
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order by r.year
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) as avg_rent_prev_year,
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lag(r.vacancy_rate, 1) over (
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partition by r.zone_code, r.bedroom_type
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order by r.year
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) as vacancy_rate_prev_year
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from rentals r
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),
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final as (
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select
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rental_id,
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date_key,
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full_date,
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year,
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quarter,
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zone_key,
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zone_code,
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zone_name,
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bedroom_type,
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rental_universe,
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avg_rent,
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median_rent,
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vacancy_rate,
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availability_rate,
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turnover_rate,
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year_over_year_rent_change,
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reliability_code,
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vacant_units_estimate,
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-- Calculated year-over-year (if not provided)
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coalesce(
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year_over_year_rent_change,
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case
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when avg_rent_prev_year > 0
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then round(((avg_rent - avg_rent_prev_year) / avg_rent_prev_year) * 100, 2)
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else null
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end
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) as rent_change_pct,
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vacancy_rate - vacancy_rate_prev_year as vacancy_rate_change
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from with_yoy
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)
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select * from final
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