lmiranda
fcaefabce8
- Remove DimTRREBDistrict model and FactPurchases model - Remove TRREBDistrict schema and AreaType enum - Remove TRREBDistrictParser from geo parsers - Remove load_trreb_districts from dimension loaders - Remove create_district_map from choropleth figures - Remove get_demo_districts and get_demo_purchase_data from demo_data - Update summary metrics to remove purchase-related metrics - Update callbacks to remove TRREB-related comments - Update methodology page to remove TRREB data source section - Update dashboard data notice to remove TRREB mention Closes #49 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
149 lines
5.0 KiB
Python
149 lines
5.0 KiB
Python
"""Demo/sample data for testing the Toronto Housing Dashboard without full pipeline.
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This module provides synthetic data for development and demonstration purposes.
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Replace with real data from the database in production.
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"""
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from datetime import date
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from typing import Any
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def get_demo_rental_data() -> list[dict[str, Any]]:
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"""Return sample rental data for visualization."""
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data = []
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zones = [
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("Zone01", "Downtown"),
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("Zone02", "Midtown"),
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("Zone03", "North York"),
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("Zone04", "Scarborough"),
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("Zone05", "Etobicoke"),
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]
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bedroom_types = ["bachelor", "1_bedroom", "2_bedroom", "3_bedroom"]
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base_rents = {
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"bachelor": 1800,
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"1_bedroom": 2200,
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"2_bedroom": 2800,
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"3_bedroom": 3400,
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}
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for year in [2021, 2022, 2023, 2024, 2025]:
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for zone_code, zone_name in zones:
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for bedroom in bedroom_types:
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# Rental trend: ~5% increase per year
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year_factor = 1 + ((year - 2021) * 0.05)
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base_rent = base_rents[bedroom]
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data.append(
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{
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"zone_code": zone_code,
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"zone_name": zone_name,
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"survey_year": year,
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"full_date": date(year, 10, 1),
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"bedroom_type": bedroom,
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"average_rent": int(base_rent * year_factor),
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"median_rent": int(base_rent * year_factor * 0.98),
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"vacancy_rate": round(
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2.5 - (year - 2021) * 0.3, 1
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), # Decreasing vacancy
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"universe": 5000 + (year - 2021) * 200,
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}
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)
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return data
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def get_demo_policy_events() -> list[dict[str, Any]]:
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"""Return sample policy events for annotation."""
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return [
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{
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"event_date": date(2024, 6, 5),
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"effective_date": date(2024, 6, 5),
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"level": "federal",
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"category": "monetary",
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"title": "BoC Rate Cut (25bp)",
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"description": "Bank of Canada cuts overnight rate by 25 basis points to 4.75%",
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"expected_direction": "bullish",
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},
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{
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"event_date": date(2024, 7, 24),
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"effective_date": date(2024, 7, 24),
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"level": "federal",
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"category": "monetary",
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"title": "BoC Rate Cut (25bp)",
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"description": "Bank of Canada cuts overnight rate by 25 basis points to 4.50%",
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"expected_direction": "bullish",
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},
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{
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"event_date": date(2024, 9, 4),
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"effective_date": date(2024, 9, 4),
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"level": "federal",
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"category": "monetary",
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"title": "BoC Rate Cut (25bp)",
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"description": "Bank of Canada cuts overnight rate by 25 basis points to 4.25%",
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"expected_direction": "bullish",
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},
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{
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"event_date": date(2024, 10, 23),
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"effective_date": date(2024, 10, 23),
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"level": "federal",
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"category": "monetary",
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"title": "BoC Rate Cut (50bp)",
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"description": "Bank of Canada cuts overnight rate by 50 basis points to 3.75%",
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"expected_direction": "bullish",
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},
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{
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"event_date": date(2024, 12, 11),
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"effective_date": date(2024, 12, 11),
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"level": "federal",
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"category": "monetary",
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"title": "BoC Rate Cut (50bp)",
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"description": "Bank of Canada cuts overnight rate by 50 basis points to 3.25%",
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"expected_direction": "bullish",
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},
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{
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"event_date": date(2024, 9, 16),
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"effective_date": date(2024, 12, 15),
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"level": "federal",
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"category": "regulatory",
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"title": "CMHC 30-Year Amortization",
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"description": "30-year amortization extended to all first-time buyers and new builds",
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"expected_direction": "bullish",
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},
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{
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"event_date": date(2024, 9, 16),
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"effective_date": date(2024, 12, 15),
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"level": "federal",
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"category": "regulatory",
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"title": "Insured Mortgage Cap $1.5M",
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"description": "Insured mortgage cap raised from $1M to $1.5M",
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"expected_direction": "bullish",
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},
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]
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def get_demo_summary_metrics() -> dict[str, dict[str, Any]]:
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"""Return summary metrics for KPI cards."""
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return {
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"avg_rent": {
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"value": 2450,
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"title": "Avg. Rent (2025)",
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"delta": 3.2,
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"delta_suffix": "%",
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"prefix": "$",
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"format_spec": ",.0f",
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"positive_is_good": False,
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},
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"vacancy_rate": {
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"value": 1.8,
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"title": "Vacancy Rate",
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"delta": -0.4,
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"delta_suffix": "pp",
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"suffix": "%",
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"format_spec": ".1f",
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"positive_is_good": False,
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},
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}
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