lmiranda
140d3085bf
- Add policy event markers to time series charts - Create methodology page (/toronto/methodology) with data sources - Add demo data module for testing without full pipeline - Update README with project documentation - Add health check endpoint (/health) - Add database initialization script - Export new figure factory functions Closes #21 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
258 lines
9.1 KiB
Python
258 lines
9.1 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_districts() -> list[dict[str, Any]]:
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"""Return sample TRREB district data."""
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return [
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{"district_code": "W01", "district_name": "Long Branch", "area_type": "West"},
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{"district_code": "W02", "district_name": "Mimico", "area_type": "West"},
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{
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"district_code": "W03",
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"district_name": "Kingsway South",
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"area_type": "West",
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},
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{"district_code": "W04", "district_name": "Edenbridge", "area_type": "West"},
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{"district_code": "W05", "district_name": "Islington", "area_type": "West"},
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{"district_code": "W06", "district_name": "Rexdale", "area_type": "West"},
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{"district_code": "W07", "district_name": "Willowdale", "area_type": "West"},
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{"district_code": "W08", "district_name": "York", "area_type": "West"},
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{
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"district_code": "C01",
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"district_name": "Downtown Core",
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"area_type": "Central",
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},
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{"district_code": "C02", "district_name": "Annex", "area_type": "Central"},
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{
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"district_code": "C03",
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"district_name": "Forest Hill",
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"area_type": "Central",
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},
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{
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"district_code": "C04",
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"district_name": "Lawrence Park",
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"area_type": "Central",
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},
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{
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"district_code": "C06",
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"district_name": "Willowdale East",
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"area_type": "Central",
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},
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{"district_code": "C07", "district_name": "Thornhill", "area_type": "Central"},
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{"district_code": "C08", "district_name": "Waterfront", "area_type": "Central"},
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{"district_code": "E01", "district_name": "Leslieville", "area_type": "East"},
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{"district_code": "E02", "district_name": "The Beaches", "area_type": "East"},
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{"district_code": "E03", "district_name": "Danforth", "area_type": "East"},
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{"district_code": "E04", "district_name": "Birch Cliff", "area_type": "East"},
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{"district_code": "E05", "district_name": "Scarborough", "area_type": "East"},
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]
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def get_demo_purchase_data() -> list[dict[str, Any]]:
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"""Return sample purchase data for time series visualization."""
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import random
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random.seed(42)
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data = []
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base_prices = {
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"W01": 850000,
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"C01": 1200000,
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"E01": 950000,
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}
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for year in [2024, 2025]:
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for month in range(1, 13):
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if year == 2025 and month > 12:
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break
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for district, base_price in base_prices.items():
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# Add some randomness and trend
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trend = (year - 2024) * 12 + month
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price_variation = random.uniform(-0.05, 0.05)
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trend_factor = 1 + (trend * 0.002) # Slight upward trend
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avg_price = int(base_price * trend_factor * (1 + price_variation))
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sales = random.randint(50, 200)
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data.append(
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{
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"district_code": district,
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"full_date": date(year, month, 1),
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"year": year,
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"month": month,
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"avg_price": avg_price,
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"median_price": int(avg_price * 0.95),
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"sales_count": sales,
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"new_listings": int(sales * random.uniform(1.2, 1.8)),
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"active_listings": int(sales * random.uniform(2.0, 3.5)),
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"days_on_market": random.randint(15, 45),
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"sale_to_list_ratio": round(random.uniform(0.95, 1.05), 2),
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}
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)
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return data
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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 $1.5M",
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"expected_direction": "bullish",
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},
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]
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def get_demo_summary_metrics() -> dict[str, dict[str, Any]]:
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"""Return summary metrics for KPI cards."""
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return {
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"avg_price": {
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"value": 1067968,
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"title": "Avg. Price (2025)",
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"delta": -4.7,
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"delta_suffix": "%",
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"prefix": "$",
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"format_spec": ",.0f",
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"positive_is_good": True,
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},
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"total_sales": {
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"value": 67610,
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"title": "Total Sales (2024)",
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"delta": 2.6,
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"delta_suffix": "%",
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"format_spec": ",.0f",
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"positive_is_good": True,
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},
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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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