lmiranda
b6d210ec6b
Create neighbourhood-centric dbt transformation layer: Staging (5 models): - stg_toronto__neighbourhoods - Neighbourhood dimension - stg_toronto__census - Census demographics - stg_toronto__crime - Crime statistics - stg_toronto__amenities - Amenity counts - stg_cmhc__zone_crosswalk - Zone-to-neighbourhood weights Intermediate (5 models): - int_neighbourhood__demographics - Combined census with quintiles - int_neighbourhood__housing - Housing + affordability indicators - int_neighbourhood__crime_summary - Aggregated crime with YoY - int_neighbourhood__amenity_scores - Per-capita amenity metrics - int_rentals__neighbourhood_allocated - CMHC via area weights Marts (5 models): - mart_neighbourhood_overview - Composite livability score - mart_neighbourhood_housing - Affordability index - mart_neighbourhood_safety - Crime rates per 100K - mart_neighbourhood_demographics - Income/age indices - mart_neighbourhood_amenities - Amenity index Closes #60, #61, #62, #63 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
74 lines
2.5 KiB
SQL
74 lines
2.5 KiB
SQL
-- Intermediate: CMHC rentals allocated to neighbourhoods via area weights
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-- Disaggregates zone-level rental data to neighbourhood level
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-- Grain: One row per neighbourhood per year
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with crosswalk as (
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select * from {{ ref('stg_cmhc__zone_crosswalk') }}
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),
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rentals as (
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select * from {{ ref('int_rentals__annual') }}
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),
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neighbourhoods as (
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select * from {{ ref('stg_toronto__neighbourhoods') }}
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),
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-- Allocate rental metrics to neighbourhoods using area weights
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allocated as (
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select
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c.neighbourhood_id,
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r.year,
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r.bedroom_type,
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-- Weighted average rent (using area weight)
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sum(r.avg_rent * c.area_weight) as weighted_avg_rent,
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sum(r.median_rent * c.area_weight) as weighted_median_rent,
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sum(c.area_weight) as total_weight,
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-- Weighted vacancy rate
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sum(r.vacancy_rate * c.area_weight) / nullif(sum(c.area_weight), 0) as vacancy_rate,
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-- Weighted rental universe
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sum(r.rental_universe * c.area_weight) as rental_units_estimate
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from crosswalk c
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inner join rentals r on c.cmhc_zone_code = r.zone_code
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group by c.neighbourhood_id, r.year, r.bedroom_type
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),
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-- Pivot to get 2-bedroom as primary metric
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pivoted as (
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select
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neighbourhood_id,
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year,
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max(case when bedroom_type = 'Two Bedroom' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_2bed,
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max(case when bedroom_type = 'One Bedroom' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_1bed,
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max(case when bedroom_type = 'Bachelor' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_bachelor,
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max(case when bedroom_type = 'Three Bedroom +' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_3bed,
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avg(vacancy_rate) as vacancy_rate,
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sum(rental_units_estimate) as total_rental_units
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from allocated
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group by neighbourhood_id, year
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),
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final as (
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select
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n.neighbourhood_id,
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n.neighbourhood_name,
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n.geometry,
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p.year,
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round(p.avg_rent_bachelor::numeric, 2) as avg_rent_bachelor,
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round(p.avg_rent_1bed::numeric, 2) as avg_rent_1bed,
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round(p.avg_rent_2bed::numeric, 2) as avg_rent_2bed,
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round(p.avg_rent_3bed::numeric, 2) as avg_rent_3bed,
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round(p.vacancy_rate::numeric, 2) as vacancy_rate,
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round(p.total_rental_units::numeric, 0) as total_rental_units
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from neighbourhoods n
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inner join pivoted p on n.neighbourhood_id = p.neighbourhood_id
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)
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select * from final
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