padelnomics/transform/sqlmesh_padelnomics/models/serving/planner_defaults.sql

-- Per-city planner defaults for the financial calculator.
-- When a user selects a city in the planner, these values pre-fill the inputs.
-- Consumed by: padelnomics.planner.routes — /api/market-data endpoint.
--
-- 3-tier data cascade:
--   1. City-level: real pricing/occupancy from Playtomic availability snapshots
--   2. Country-level: median across cities in same country
--   3. Hardcoded fallback: market research estimates (only when no Playtomic data)
--
-- Cost override columns from dim_countries (Eurostat PLI + energy price indices) are
-- included so the planner API pre-fills country-adjusted CAPEX/OPEX for all cities.
-- NULL = fall through to calculator.py DEFAULTS. DE always NULL (baseline preserved).
--
-- Units are explicit in column names. Monetary values in local currency.

MODEL (
  name serving.planner_defaults,
  kind FULL,
  cron '@daily',
  grain (country_code, city_slug)
);

WITH -- Real city-level benchmarks from Playtomic
city_benchmarks AS (
  SELECT
    country_code,
    city,
    city_slug,
    median_peak_rate,
    median_offpeak_rate,
    median_occupancy_rate,
    median_daily_revenue_per_venue,
    median_court_count,
    venue_count,
    total_venue_days_observed,
    price_currency
  FROM serving.venue_pricing_benchmarks
),
-- Country-level medians (fallback when a city has no availability data)
country_benchmarks AS (
  SELECT
    country_code,
    MEDIAN(median_peak_rate)                    AS median_peak_rate,
    MEDIAN(median_offpeak_rate)                 AS median_offpeak_rate,
    MEDIAN(median_occupancy_rate)               AS median_occupancy_rate,
    MEDIAN(median_court_count)                  AS median_court_count,
    SUM(venue_count)                            AS total_venues,
    MIN(price_currency)                         AS price_currency
  FROM city_benchmarks
  GROUP BY country_code
),
-- Hardcoded global fallbacks (only for countries with zero Playtomic coverage)
hardcoded_fallbacks AS (
  SELECT * FROM (VALUES
    ('DE', 22.0, 16.5, 0.55, 4, 'EUR'),
    ('ES', 16.0, 12.0, 0.62, 6, 'EUR'),
    ('GB', 24.0, 18.0, 0.52, 4, 'GBP'),
    ('FR', 18.0, 13.5, 0.58, 5, 'EUR'),
    ('IT', 15.0, 11.0, 0.60, 6, 'EUR'),
    ('PT', 12.0,  9.0, 0.65, 6, 'EUR'),
    ('AT', 20.0, 15.0, 0.54, 4, 'EUR'),
    ('CH', 28.0, 21.0, 0.50, 4, 'CHF'),
    ('NL', 20.0, 15.0, 0.56, 4, 'EUR'),
    ('BE', 18.0, 13.5, 0.57, 4, 'EUR'),
    ('SE', 22.0, 16.5, 0.50, 4, 'SEK'),
    ('US', 20.0, 15.0, 0.58, 6, 'USD'),
    ('MX', 12.0,  9.0, 0.55, 4, 'MXN'),
    ('AR', 10.0,  7.5, 0.60, 4, 'ARS'),
    ('DK', 24.0, 18.0, 0.48, 4, 'DKK'),
    ('NO', 26.0, 19.5, 0.45, 4, 'NOK'),
    ('FI', 22.0, 16.5, 0.48, 4, 'EUR')
  ) AS t(country_code, peak_rate, offpeak_rate, occupancy, courts, currency)
),
city_profiles AS (
  SELECT
    city_slug,
    country_code,
    city_name,
    padel_venue_count,
    population,
    market_score,
    venues_per_100k
  FROM serving.city_market_profile
)
SELECT
  cp.city_slug,
  cp.country_code,
  cp.city_name,
  cp.padel_venue_count,
  cp.population,
  cp.market_score,
  -- Peak rate: city → country → hardcoded
  ROUND(COALESCE(
    cb.median_peak_rate,
    ctb.median_peak_rate,
    hf.peak_rate
  ), 2)                                         AS rate_peak,
  -- Off-peak rate
  ROUND(COALESCE(
    cb.median_offpeak_rate,
    ctb.median_offpeak_rate,
    hf.offpeak_rate
  ), 2)                                         AS rate_off_peak,
  -- Occupancy (utilisation)
  ROUND(COALESCE(
    cb.median_occupancy_rate,
    ctb.median_occupancy_rate,
    hf.occupancy
  ), 4)                                         AS avg_utilisation_pct,
  -- Typical court count
  COALESCE(
    cb.median_court_count,
    ctb.median_court_count,
    hf.courts
  )                                             AS courts_typical,
  -- Revenue estimate (city-level only)
  cb.median_daily_revenue_per_venue             AS daily_revenue_per_venue,
  -- Data provenance
  CASE
    WHEN cb.venue_count IS NOT NULL THEN 'city_data'
    WHEN ctb.total_venues IS NOT NULL THEN 'country_data'
    ELSE 'hardcoded'
  END                                           AS data_source,
  CASE
    WHEN cb.total_venue_days_observed >= 100 THEN 1.0
    WHEN cb.total_venue_days_observed >= 30  THEN 0.8
    WHEN cb.venue_count IS NOT NULL          THEN 0.6
    WHEN ctb.total_venues IS NOT NULL        THEN 0.4
    ELSE 0.2
  END                                           AS data_confidence,
  COALESCE(cb.price_currency, ctb.price_currency, hf.currency, 'EUR') AS price_currency,
  -- Cost override columns (Eurostat PLI + energy prices via dim_countries).
  -- NULL = fall through to calculator.py DEFAULTS. DE always NULL (baseline).
  dc.electricity,
  dc.heating,
  dc.rent_sqm,
  dc.insurance,
  dc.cleaning,
  dc.maintenance,
  dc.marketing,
  dc.water,
  dc.property_tax,
  dc.outdoor_rent,
  dc.hall_cost_sqm,
  dc.foundation_sqm,
  dc.land_price_sqm,
  dc.hvac,
  dc.electrical,
  dc.sanitary,
  dc.parking,
  dc.fitout,
  dc.planning,
  dc.fire_protection,
  dc.floor_prep,
  dc.hvac_upgrade,
  dc.lighting_upgrade,
  dc.outdoor_foundation,
  dc.outdoor_site_work,
  dc.outdoor_lighting,
  dc.outdoor_fencing,
  dc.working_capital,
  dc.permits_compliance,
  CURRENT_DATE                                  AS refreshed_date
FROM city_profiles cp
LEFT JOIN city_benchmarks cb
  ON cp.country_code = cb.country_code
  AND cp.city_slug = cb.city_slug
LEFT JOIN country_benchmarks ctb
  ON cp.country_code = ctb.country_code
LEFT JOIN hardcoded_fallbacks hf
  ON cp.country_code = hf.country_code
LEFT JOIN foundation.dim_countries dc
  ON cp.country_code = dc.country_code