Merge branch 'feature/7-nav-theme-modernization' into development

- Add floating pill-shaped sidebar with navigation icons
- Implement dark/light theme toggle with localStorage persistence
- Update all figure factories for transparent backgrounds
- Use carto-darkmatter map style for choropleths
- Add methodology link button to Toronto dashboard header
- Add back to dashboard button on methodology page
- Remove social links from home page (now in sidebar)
- Update CLAUDE.md to Sprint 7

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.env.example

@@ -0,0 +1,15 @@
+# Database Configuration
+DATABASE_URL=postgresql://portfolio:portfolio_dev@localhost:5432/portfolio
+POSTGRES_USER=portfolio
+POSTGRES_PASSWORD=portfolio_dev
+POSTGRES_DB=portfolio
+
+# Application Settings
+DASH_DEBUG=true
+SECRET_KEY=change-me-in-production
+
+# Logging
+LOG_LEVEL=INFO
+
+# Optional: dbt profile (defaults to profiles.yml)
+# DBT_PROFILES_DIR=.

.gitignore

@@ -1,4 +1,28 @@
-# ---> Python
+# ====================
+# Project-Specific
+# ====================
+
+# Processed data (generated, not source)
+data/*/processed/
+
+# Reports (generated)
+reports/
+
+# Backups
+backups/
+
+# Notebook exports
+notebooks/*.html
+
+# dbt
+dbt/target/
+dbt/dbt_packages/
+dbt/logs/
+
+# ====================
+# Python
+# ====================
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

.pre-commit-config.yaml

@@ -0,0 +1,33 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-added-large-files
+        args: ['--maxkb=1000']
+        exclude: ^data/(raw/|toronto/raw/geo/)
+      - id: check-merge-conflict
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.1.9
+    hooks:
+      - id: ruff
+        args: [--fix, --exit-non-zero-on-fix]
+      - id: ruff-format
+
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.8.0
+    hooks:
+      - id: mypy
+        additional_dependencies:
+          - pydantic>=2.0
+          - pandas-stubs
+          - types-requests
+        args: [--ignore-missing-imports]
+        exclude: ^(tests/|dbt/)
+
+ci:
+  autofix_commit_msg: "style: auto-fix by pre-commit hooks"
+  autoupdate_commit_msg: "chore: update pre-commit hooks"

.python-version

@@ -0,0 +1 @@
+3.11

CLAUDE.md

@@ -0,0 +1,257 @@
+# CLAUDE.md
+
+Working context for Claude Code on the Analytics Portfolio project.
+
+---
+
+## Project Status
+
+**Current Sprint**: 7 (Navigation & Theme Modernization)
+**Phase**: 1 - Toronto Housing Dashboard
+**Branch**: `development` (feature branches merge here)
+
+---
+
+## Quick Reference
+
+### Run Commands
+
+```bash
+make setup          # Install deps, create .env, init pre-commit
+make docker-up      # Start PostgreSQL + PostGIS
+make docker-down    # Stop containers
+make db-init        # Initialize database schema
+make run            # Start Dash dev server
+make test           # Run pytest
+make lint           # Run ruff linter
+make format         # Run ruff formatter
+make ci             # Run all checks
+```
+
+### Branch Workflow
+
+1. Create feature branch FROM `development`: `git checkout -b feature/{sprint}-{description}`
+2. Work and commit on feature branch
+3. Merge INTO `development` when complete
+4. `development` -> `staging` -> `main` for releases
+
+---
+
+## Code Conventions
+
+### Import Style
+
+| Context | Style | Example |
+|---------|-------|---------|
+| Same directory | Single dot | `from .trreb import TRREBParser` |
+| Sibling directory | Double dot | `from ..schemas.trreb import TRREBRecord` |
+| External packages | Absolute | `import pandas as pd` |
+
+### Module Responsibilities
+
+| Directory | Contains | Purpose |
+|-----------|----------|---------|
+| `schemas/` | Pydantic models | Data validation |
+| `models/` | SQLAlchemy ORM | Database persistence |
+| `parsers/` | PDF/CSV extraction | Raw data ingestion |
+| `loaders/` | Database operations | Data loading |
+| `figures/` | Chart factories | Plotly figure generation |
+| `callbacks/` | Dash callbacks | In `pages/{dashboard}/callbacks/` |
+| `errors/` | Exceptions + handlers | Error handling |
+
+### Type Hints
+
+Use Python 3.10+ style:
+```python
+def process(items: list[str], config: dict[str, int] | None = None) -> bool:
+    ...
+```
+
+### Error Handling
+
+```python
+# errors/exceptions.py
+class PortfolioError(Exception):
+    """Base exception."""
+
+class ParseError(PortfolioError):
+    """PDF/CSV parsing failed."""
+
+class ValidationError(PortfolioError):
+    """Pydantic or business rule validation failed."""
+
+class LoadError(PortfolioError):
+    """Database load operation failed."""
+```
+
+### Code Standards
+
+- Single responsibility functions with verb naming
+- Early returns over deep nesting
+- Google-style docstrings only for non-obvious behavior
+- Module-level constants for magic values
+- Pydantic BaseSettings for runtime config
+
+---
+
+## Application Structure
+
+```
+portfolio_app/
+├── app.py                    # Dash app factory with Pages routing
+├── config.py                 # Pydantic BaseSettings
+├── assets/                   # CSS, images (auto-served)
+├── pages/
+│   ├── home.py              # Bio landing page -> /
+│   └── toronto/
+│       ├── dashboard.py     # Layout only -> /toronto
+│       └── callbacks/       # Interaction logic
+├── components/              # Shared UI (navbar, footer, cards)
+├── figures/                 # Shared chart factories
+├── toronto/                 # Toronto data logic
+│   ├── parsers/
+│   ├── loaders/
+│   ├── schemas/             # Pydantic
+│   └── models/              # SQLAlchemy
+└── errors/
+```
+
+### URL Routing
+
+| URL | Page | Sprint |
+|-----|------|--------|
+| `/` | Bio landing page | 2 |
+| `/toronto` | Toronto Housing Dashboard | 6 |
+
+---
+
+## Tech Stack (Locked)
+
+| Layer | Technology | Version |
+|-------|------------|---------|
+| Database | PostgreSQL + PostGIS | 16.x |
+| Validation | Pydantic | >=2.0 |
+| ORM | SQLAlchemy | >=2.0 (2.0-style API only) |
+| Transformation | dbt-postgres | >=1.7 |
+| Data Processing | Pandas | >=2.1 |
+| Geospatial | GeoPandas + Shapely | >=0.14 |
+| Visualization | Dash + Plotly | >=2.14 |
+| UI Components | dash-mantine-components | Latest stable |
+| Testing | pytest | >=7.0 |
+| Python | 3.11+ | Via pyenv |
+
+**Notes**:
+- SQLAlchemy 2.0 + Pydantic 2.0 only (never mix 1.x APIs)
+- PostGIS extension required in database
+- Docker Compose V2 format (no `version` field)
+
+---
+
+## Data Model Overview
+
+### Geographic Reality (Toronto Housing)
+
+```
+TRREB Districts (~35)     - Purchase data (W01, C01, E01...)
+CMHC Zones (~20)          - Rental data (Census Tract aligned)
+City Neighbourhoods (158) - Enrichment/overlay only
+```
+
+**Critical**: These geographies do NOT align. Display as separate layers—do not force crosswalks.
+
+### Star Schema
+
+| Table | Type | Keys |
+|-------|------|------|
+| `fact_purchases` | Fact | -> dim_time, dim_trreb_district |
+| `fact_rentals` | Fact | -> dim_time, dim_cmhc_zone |
+| `dim_time` | Dimension | date_key (PK) |
+| `dim_trreb_district` | Dimension | district_key (PK), geometry |
+| `dim_cmhc_zone` | Dimension | zone_key (PK), geometry |
+| `dim_neighbourhood` | Dimension | neighbourhood_id (PK), geometry |
+| `dim_policy_event` | Dimension | event_id (PK) |
+
+**V1 Rule**: `dim_neighbourhood` has NO FK to fact tables—reference overlay only.
+
+### dbt Layers
+
+| Layer | Naming | Purpose |
+|-------|--------|---------|
+| Staging | `stg_{source}__{entity}` | 1:1 source, cleaned, typed |
+| Intermediate | `int_{domain}__{transform}` | Business logic |
+| Marts | `mart_{domain}` | Final analytical tables |
+
+---
+
+## DO NOT BUILD (Phase 1)
+
+**Stop and flag if a task seems to require these**:
+
+| Feature | Reason |
+|---------|--------|
+| `bridge_district_neighbourhood` table | Area-weighted aggregation is Phase 4 |
+| Crime data integration | Deferred to Phase 4 |
+| Historical boundary reconciliation (140->158) | 2021+ data only for V1 |
+| ML prediction models | Energy project scope (Phase 3) |
+| Multi-project shared infrastructure | Build first, abstract second (Phase 2) |
+
+---
+
+## Sprint 1 Deliverables
+
+| Category | Tasks |
+|----------|-------|
+| **Bootstrap** | Git init, pyproject.toml, .env.example, Makefile, CLAUDE.md |
+| **Infrastructure** | Docker Compose (PostgreSQL + PostGIS), scripts/ directory |
+| **App Foundation** | portfolio_app/ structure, config.py, error handling |
+| **Tests** | tests/ directory, conftest.py, pytest config |
+| **Data Acquisition** | Download TRREB PDFs, START boundary digitization (HUMAN task) |
+
+### Human Tasks (Cannot Automate)
+
+| Task | Tool | Effort |
+|------|------|--------|
+| Digitize TRREB district boundaries | QGIS | 3-4 hours |
+| Research policy events (10-20) | Manual | 2-3 hours |
+| Replace social link placeholders | Manual | 5 minutes |
+
+---
+
+## Environment Variables
+
+Required in `.env`:
+
+```bash
+DATABASE_URL=postgresql://user:pass@localhost:5432/portfolio
+POSTGRES_USER=portfolio
+POSTGRES_PASSWORD=<secure>
+POSTGRES_DB=portfolio
+DASH_DEBUG=true
+SECRET_KEY=<random>
+LOG_LEVEL=INFO
+```
+
+---
+
+## Script Standards
+
+All scripts in `scripts/`:
+- Include usage comments at top
+- Idempotent where possible
+- Exit codes: 0 = success, 1 = error
+- Use `set -euo pipefail` for bash
+- Log to stdout, errors to stderr
+
+---
+
+## Reference Documents
+
+| Document | Location | Use When |
+|----------|----------|----------|
+| Full specification | `docs/PROJECT_REFERENCE.md` | Architecture decisions |
+| Data schemas | `docs/toronto_housing_dashboard_spec_v5.md` | Parser/model tasks |
+| WBS details | `docs/wbs_sprint_plan_v4.md` | Sprint planning |
+
+---
+
+*Last Updated: Sprint 7*

Makefile

@@ -0,0 +1,157 @@
+.PHONY: setup docker-up docker-down db-init run test dbt-run dbt-test lint format ci deploy clean help
+
+# Default target
+.DEFAULT_GOAL := help
+
+# Environment
+PYTHON := python3
+PIP := pip
+DOCKER_COMPOSE := docker compose
+
+# Colors for output
+BLUE := \033[0;34m
+GREEN := \033[0;32m
+YELLOW := \033[0;33m
+NC := \033[0m
+
+help: ## Show this help message
+	@echo "Usage: make [target]"
+	@echo ""
+	@echo "Targets:"
+	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "  $(BLUE)%-15s$(NC) %s\n", $$1, $$2}'
+
+# =============================================================================
+# Setup
+# =============================================================================
+
+setup: ## Install dependencies, create .env, init pre-commit
+	@echo "$(GREEN)Installing dependencies...$(NC)"
+	$(PIP) install -e ".[dev,dbt]"
+	@echo "$(GREEN)Setting up environment...$(NC)"
+	@if [ ! -f .env ]; then cp .env.example .env; echo "$(YELLOW)Created .env from .env.example - please update values$(NC)"; fi
+	@echo "$(GREEN)Installing pre-commit hooks...$(NC)"
+	pre-commit install
+	@echo "$(GREEN)Setup complete!$(NC)"
+
+# =============================================================================
+# Docker
+# =============================================================================
+
+docker-up: ## Start PostgreSQL + PostGIS containers
+	@echo "$(GREEN)Starting database containers...$(NC)"
+	$(DOCKER_COMPOSE) up -d
+	@echo "$(GREEN)Waiting for database to be ready...$(NC)"
+	@sleep 3
+	@echo "$(GREEN)Database containers started!$(NC)"
+
+docker-down: ## Stop containers
+	@echo "$(YELLOW)Stopping containers...$(NC)"
+	$(DOCKER_COMPOSE) down
+
+docker-logs: ## View container logs
+	$(DOCKER_COMPOSE) logs -f
+
+# =============================================================================
+# Database
+# =============================================================================
+
+db-init: ## Initialize database schema
+	@echo "$(GREEN)Initializing database schema...$(NC)"
+	@if [ -f scripts/db/init.sh ]; then \
+		bash scripts/db/init.sh; \
+	else \
+		echo "$(YELLOW)scripts/db/init.sh not found - skipping$(NC)"; \
+	fi
+
+db-reset: ## Drop and recreate database (DESTRUCTIVE)
+	@echo "$(YELLOW)WARNING: This will delete all data!$(NC)"
+	@read -p "Are you sure? [y/N] " confirm && [ "$$confirm" = "y" ] || exit 1
+	$(DOCKER_COMPOSE) down -v
+	$(DOCKER_COMPOSE) up -d
+	@sleep 3
+	$(MAKE) db-init
+
+# =============================================================================
+# Application
+# =============================================================================
+
+run: ## Start Dash development server
+	@echo "$(GREEN)Starting Dash server...$(NC)"
+	$(PYTHON) -m portfolio_app.app
+
+# =============================================================================
+# Testing
+# =============================================================================
+
+test: ## Run pytest
+	@echo "$(GREEN)Running tests...$(NC)"
+	pytest
+
+test-cov: ## Run pytest with coverage
+	@echo "$(GREEN)Running tests with coverage...$(NC)"
+	pytest --cov=portfolio_app --cov-report=html --cov-report=term
+
+# =============================================================================
+# dbt
+# =============================================================================
+
+dbt-run: ## Run dbt models
+	@echo "$(GREEN)Running dbt models...$(NC)"
+	cd dbt && dbt run
+
+dbt-test: ## Run dbt tests
+	@echo "$(GREEN)Running dbt tests...$(NC)"
+	cd dbt && dbt test
+
+dbt-docs: ## Generate dbt documentation
+	@echo "$(GREEN)Generating dbt docs...$(NC)"
+	cd dbt && dbt docs generate && dbt docs serve
+
+# =============================================================================
+# Code Quality
+# =============================================================================
+
+lint: ## Run ruff linter
+	@echo "$(GREEN)Running linter...$(NC)"
+	ruff check .
+
+format: ## Run ruff formatter
+	@echo "$(GREEN)Formatting code...$(NC)"
+	ruff format .
+	ruff check --fix .
+
+typecheck: ## Run mypy type checker
+	@echo "$(GREEN)Running type checker...$(NC)"
+	mypy portfolio_app
+
+ci: ## Run all checks (lint, typecheck, test)
+	@echo "$(GREEN)Running CI checks...$(NC)"
+	$(MAKE) lint
+	$(MAKE) typecheck
+	$(MAKE) test
+	@echo "$(GREEN)All checks passed!$(NC)"
+
+# =============================================================================
+# Deployment
+# =============================================================================
+
+deploy: ## Deploy to production
+	@echo "$(YELLOW)Deployment not yet configured$(NC)"
+	@echo "TODO: Add deployment script"
+
+# =============================================================================
+# Cleanup
+# =============================================================================
+
+clean: ## Remove build artifacts and caches
+	@echo "$(YELLOW)Cleaning up...$(NC)"
+	rm -rf build/
+	rm -rf dist/
+	rm -rf *.egg-info/
+	rm -rf .pytest_cache/
+	rm -rf .ruff_cache/
+	rm -rf .mypy_cache/
+	rm -rf htmlcov/
+	rm -rf .coverage
+	find . -type d -name "__pycache__" -exec rm -rf {} + 2>/dev/null || true
+	@echo "$(GREEN)Clean complete!$(NC)"

README.md

@@ -1,2 +1,120 @@
-# personal-portfolio
+# Analytics Portfolio
 
+A data analytics portfolio showcasing end-to-end data engineering, visualization, and analysis capabilities.
+
+## Projects
+
+### Toronto Housing Dashboard
+
+An interactive choropleth dashboard analyzing Toronto's housing market using multi-source data integration.
+
+**Features:**
+- Purchase market analysis from TRREB monthly reports
+- Rental market analysis from CMHC annual surveys
+- Interactive choropleth maps by district/zone
+- Time series visualization with policy event annotations
+- Purchase/Rental mode toggle
+
+**Data Sources:**
+- [TRREB Market Watch](https://trreb.ca/market-data/market-watch/) - Monthly purchase statistics
+- [CMHC Rental Market Survey](https://www.cmhc-schl.gc.ca/professionals/housing-markets-data-and-research/housing-data/data-tables/rental-market) - Annual rental data
+
+**Tech Stack:**
+- Python 3.11+ / Dash / Plotly
+- PostgreSQL + PostGIS
+- dbt for data transformation
+- Pydantic for validation
+- SQLAlchemy 2.0
+
+## Quick Start
+
+```bash
+# Clone and setup
+git clone https://github.com/lmiranda/personal-portfolio.git
+cd personal-portfolio
+
+# Install dependencies and configure environment
+make setup
+
+# Start database
+make docker-up
+
+# Initialize database schema
+make db-init
+
+# Run development server
+make run
+```
+
+Visit `http://localhost:8050` to view the portfolio.
+
+## Project Structure
+
+```
+portfolio_app/
+├── app.py                 # Dash app factory
+├── config.py              # Pydantic settings
+├── pages/
+│   ├── home.py           # Bio landing page (/)
+│   └── toronto/          # Toronto dashboard (/toronto)
+├── components/            # Shared UI components
+├── figures/               # Plotly figure factories
+└── toronto/               # Toronto data logic
+    ├── parsers/          # PDF/CSV extraction
+    ├── loaders/          # Database operations
+    ├── schemas/          # Pydantic models
+    └── models/           # SQLAlchemy ORM
+
+dbt/
+├── models/
+│   ├── staging/          # 1:1 source tables
+│   ├── intermediate/     # Business logic
+│   └── marts/            # Analytical tables
+```
+
+## Development
+
+```bash
+make test      # Run tests
+make lint      # Run linter
+make format    # Format code
+make ci        # Run all checks
+```
+
+## Data Pipeline
+
+```
+Raw Files (PDF/Excel)
+    ↓
+Parsers (pdfplumber, pandas)
+    ↓
+Pydantic Validation
+    ↓
+SQLAlchemy Loaders
+    ↓
+PostgreSQL + PostGIS
+    ↓
+dbt Transformations
+    ↓
+Dash Visualization
+```
+
+## Environment Variables
+
+Copy `.env.example` to `.env` and configure:
+
+```bash
+DATABASE_URL=postgresql://user:pass@localhost:5432/portfolio
+POSTGRES_USER=portfolio
+POSTGRES_PASSWORD=<secure>
+POSTGRES_DB=portfolio
+DASH_DEBUG=true
+```
+
+## License
+
+MIT
+
+## Author
+
+Leo Miranda - [GitHub](https://github.com/lmiranda) | [LinkedIn](https://linkedin.com/in/yourprofile)

dbt/dbt_project.yml

@@ -0,0 +1,28 @@
+name: 'toronto_housing'
+version: '1.0.0'
+config-version: 2
+
+profile: 'toronto_housing'
+
+model-paths: ["models"]
+analysis-paths: ["analyses"]
+test-paths: ["tests"]
+seed-paths: ["seeds"]
+macro-paths: ["macros"]
+snapshot-paths: ["snapshots"]
+
+clean-targets:
+  - "target"
+  - "dbt_packages"
+
+models:
+  toronto_housing:
+    staging:
+      +materialized: view
+      +schema: staging
+    intermediate:
+      +materialized: view
+      +schema: intermediate
+    marts:
+      +materialized: table
+      +schema: marts

dbt/models/intermediate/_intermediate.yml

@@ -0,0 +1,24 @@
+version: 2
+
+models:
+  - name: int_purchases__monthly
+    description: "Purchase data enriched with time and district dimensions"
+    columns:
+      - name: purchase_id
+        tests:
+          - unique
+          - not_null
+      - name: district_code
+        tests:
+          - not_null
+
+  - name: int_rentals__annual
+    description: "Rental data enriched with time and zone dimensions"
+    columns:
+      - name: rental_id
+        tests:
+          - unique
+          - not_null
+      - name: zone_code
+        tests:
+          - not_null

dbt/models/intermediate/int_purchases__monthly.sql

@@ -0,0 +1,62 @@
+-- Intermediate: Monthly purchase data enriched with dimensions
+-- Joins purchases with time and district dimensions for analysis
+
+with purchases as (
+    select * from {{ ref('stg_trreb__purchases') }}
+),
+
+time_dim as (
+    select * from {{ ref('stg_dimensions__time') }}
+),
+
+district_dim as (
+    select * from {{ ref('stg_dimensions__trreb_districts') }}
+),
+
+enriched as (
+    select
+        p.purchase_id,
+
+        -- Time attributes
+        t.date_key,
+        t.full_date,
+        t.year,
+        t.month,
+        t.quarter,
+        t.month_name,
+
+        -- District attributes
+        d.district_key,
+        d.district_code,
+        d.district_name,
+        d.area_type,
+
+        -- Metrics
+        p.sales_count,
+        p.dollar_volume,
+        p.avg_price,
+        p.median_price,
+        p.new_listings,
+        p.active_listings,
+        p.days_on_market,
+        p.sale_to_list_ratio,
+
+        -- Calculated metrics
+        case
+            when p.active_listings > 0
+            then round(p.sales_count::numeric / p.active_listings, 3)
+            else null
+        end as absorption_rate,
+
+        case
+            when p.sales_count > 0
+            then round(p.active_listings::numeric / p.sales_count, 1)
+            else null
+        end as months_of_inventory
+
+    from purchases p
+    inner join time_dim t on p.date_key = t.date_key
+    inner join district_dim d on p.district_key = d.district_key
+)
+
+select * from enriched

dbt/models/intermediate/int_rentals__annual.sql

@@ -0,0 +1,57 @@
+-- Intermediate: Annual rental data enriched with dimensions
+-- Joins rentals with time and zone dimensions for analysis
+
+with rentals as (
+    select * from {{ ref('stg_cmhc__rentals') }}
+),
+
+time_dim as (
+    select * from {{ ref('stg_dimensions__time') }}
+),
+
+zone_dim as (
+    select * from {{ ref('stg_dimensions__cmhc_zones') }}
+),
+
+enriched as (
+    select
+        r.rental_id,
+
+        -- Time attributes
+        t.date_key,
+        t.full_date,
+        t.year,
+        t.month,
+        t.quarter,
+
+        -- Zone attributes
+        z.zone_key,
+        z.zone_code,
+        z.zone_name,
+
+        -- Bedroom type
+        r.bedroom_type,
+
+        -- Metrics
+        r.rental_universe,
+        r.avg_rent,
+        r.median_rent,
+        r.vacancy_rate,
+        r.availability_rate,
+        r.turnover_rate,
+        r.year_over_year_rent_change,
+        r.reliability_code,
+
+        -- Calculated metrics
+        case
+            when r.rental_universe > 0 and r.vacancy_rate is not null
+            then round(r.rental_universe * (r.vacancy_rate / 100), 0)
+            else null
+        end as vacant_units_estimate
+
+    from rentals r
+    inner join time_dim t on r.date_key = t.date_key
+    inner join zone_dim z on r.zone_key = z.zone_key
+)
+
+select * from enriched

dbt/models/marts/_marts.yml

@@ -0,0 +1,23 @@
+version: 2
+
+models:
+  - name: mart_toronto_purchases
+    description: "Final mart for Toronto purchase/sales analysis by district and time"
+    columns:
+      - name: purchase_id
+        description: "Unique purchase record identifier"
+        tests:
+          - unique
+          - not_null
+
+  - name: mart_toronto_rentals
+    description: "Final mart for Toronto rental market analysis by zone and time"
+    columns:
+      - name: rental_id
+        description: "Unique rental record identifier"
+        tests:
+          - unique
+          - not_null
+
+  - name: mart_toronto_market_summary
+    description: "Combined market summary aggregating purchases and rentals at Toronto level"

dbt/models/marts/mart_toronto_market_summary.sql

@@ -0,0 +1,81 @@
+-- Mart: Toronto Market Summary
+-- Aggregated view combining purchase and rental market indicators
+-- Grain: One row per year-month
+
+with purchases_agg as (
+    select
+        year,
+        month,
+        month_name,
+        quarter,
+
+        -- Aggregate purchase metrics across all districts
+        sum(sales_count) as total_sales,
+        sum(dollar_volume) as total_dollar_volume,
+        round(avg(avg_price), 0) as avg_price_all_districts,
+        round(avg(median_price), 0) as median_price_all_districts,
+        sum(new_listings) as total_new_listings,
+        sum(active_listings) as total_active_listings,
+        round(avg(days_on_market), 0) as avg_days_on_market,
+        round(avg(sale_to_list_ratio), 2) as avg_sale_to_list_ratio,
+        round(avg(absorption_rate), 3) as avg_absorption_rate,
+        round(avg(months_of_inventory), 1) as avg_months_of_inventory,
+        round(avg(avg_price_yoy_pct), 2) as avg_price_yoy_pct
+
+    from {{ ref('mart_toronto_purchases') }}
+    group by year, month, month_name, quarter
+),
+
+rentals_agg as (
+    select
+        year,
+
+        -- Aggregate rental metrics across all zones (all bedroom types)
+        round(avg(avg_rent), 0) as avg_rent_all_zones,
+        round(avg(vacancy_rate), 2) as avg_vacancy_rate,
+        round(avg(rent_change_pct), 2) as avg_rent_change_pct,
+        sum(rental_universe) as total_rental_universe
+
+    from {{ ref('mart_toronto_rentals') }}
+    group by year
+),
+
+final as (
+    select
+        p.year,
+        p.month,
+        p.month_name,
+        p.quarter,
+
+        -- Purchase market indicators
+        p.total_sales,
+        p.total_dollar_volume,
+        p.avg_price_all_districts,
+        p.median_price_all_districts,
+        p.total_new_listings,
+        p.total_active_listings,
+        p.avg_days_on_market,
+        p.avg_sale_to_list_ratio,
+        p.avg_absorption_rate,
+        p.avg_months_of_inventory,
+        p.avg_price_yoy_pct,
+
+        -- Rental market indicators (annual, so join on year)
+        r.avg_rent_all_zones,
+        r.avg_vacancy_rate,
+        r.avg_rent_change_pct,
+        r.total_rental_universe,
+
+        -- Affordability indicator (price to rent ratio)
+        case
+            when r.avg_rent_all_zones > 0
+            then round(p.avg_price_all_districts / (r.avg_rent_all_zones * 12), 1)
+            else null
+        end as price_to_annual_rent_ratio
+
+    from purchases_agg p
+    left join rentals_agg r on p.year = r.year
+)
+
+select * from final
+order by year desc, month desc

dbt/models/marts/mart_toronto_purchases.sql

@@ -0,0 +1,79 @@
+-- Mart: Toronto Purchase Market Analysis
+-- Final analytical table for purchase/sales data visualization
+-- Grain: One row per district per month
+
+with purchases as (
+    select * from {{ ref('int_purchases__monthly') }}
+),
+
+-- Add year-over-year calculations
+with_yoy as (
+    select
+        p.*,
+
+        -- Previous year same month values
+        lag(p.avg_price, 12) over (
+            partition by p.district_code
+            order by p.date_key
+        ) as avg_price_prev_year,
+
+        lag(p.sales_count, 12) over (
+            partition by p.district_code
+            order by p.date_key
+        ) as sales_count_prev_year,
+
+        lag(p.median_price, 12) over (
+            partition by p.district_code
+            order by p.date_key
+        ) as median_price_prev_year
+
+    from purchases p
+),
+
+final as (
+    select
+        purchase_id,
+        date_key,
+        full_date,
+        year,
+        month,
+        quarter,
+        month_name,
+        district_key,
+        district_code,
+        district_name,
+        area_type,
+        sales_count,
+        dollar_volume,
+        avg_price,
+        median_price,
+        new_listings,
+        active_listings,
+        days_on_market,
+        sale_to_list_ratio,
+        absorption_rate,
+        months_of_inventory,
+
+        -- Year-over-year changes
+        case
+            when avg_price_prev_year > 0
+            then round(((avg_price - avg_price_prev_year) / avg_price_prev_year) * 100, 2)
+            else null
+        end as avg_price_yoy_pct,
+
+        case
+            when sales_count_prev_year > 0
+            then round(((sales_count - sales_count_prev_year)::numeric / sales_count_prev_year) * 100, 2)
+            else null
+        end as sales_count_yoy_pct,
+
+        case
+            when median_price_prev_year > 0
+            then round(((median_price - median_price_prev_year) / median_price_prev_year) * 100, 2)
+            else null
+        end as median_price_yoy_pct
+
+    from with_yoy
+)
+
+select * from final

dbt/models/marts/mart_toronto_rentals.sql

@@ -0,0 +1,64 @@
+-- Mart: Toronto Rental Market Analysis
+-- Final analytical table for rental market visualization
+-- Grain: One row per zone per bedroom type per survey year
+
+with rentals as (
+    select * from {{ ref('int_rentals__annual') }}
+),
+
+-- Add year-over-year calculations
+with_yoy as (
+    select
+        r.*,
+
+        -- Previous year values
+        lag(r.avg_rent, 1) over (
+            partition by r.zone_code, r.bedroom_type
+            order by r.year
+        ) as avg_rent_prev_year,
+
+        lag(r.vacancy_rate, 1) over (
+            partition by r.zone_code, r.bedroom_type
+            order by r.year
+        ) as vacancy_rate_prev_year
+
+    from rentals r
+),
+
+final as (
+    select
+        rental_id,
+        date_key,
+        full_date,
+        year,
+        quarter,
+        zone_key,
+        zone_code,
+        zone_name,
+        bedroom_type,
+        rental_universe,
+        avg_rent,
+        median_rent,
+        vacancy_rate,
+        availability_rate,
+        turnover_rate,
+        year_over_year_rent_change,
+        reliability_code,
+        vacant_units_estimate,
+
+        -- Calculated year-over-year (if not provided)
+        coalesce(
+            year_over_year_rent_change,
+            case
+                when avg_rent_prev_year > 0
+                then round(((avg_rent - avg_rent_prev_year) / avg_rent_prev_year) * 100, 2)
+                else null
+            end
+        ) as rent_change_pct,
+
+        vacancy_rate - vacancy_rate_prev_year as vacancy_rate_change
+
+    from with_yoy
+)
+
+select * from final

dbt/models/staging/_sources.yml

@@ -0,0 +1,61 @@
+version: 2
+
+sources:
+  - name: toronto_housing
+    description: "Toronto housing data loaded from TRREB and CMHC sources"
+    database: portfolio
+    schema: public
+    tables:
+      - name: fact_purchases
+        description: "TRREB monthly purchase/sales statistics by district"
+        columns:
+          - name: id
+            description: "Primary key"
+          - name: date_key
+            description: "Foreign key to dim_time"
+          - name: district_key
+            description: "Foreign key to dim_trreb_district"
+
+      - name: fact_rentals
+        description: "CMHC annual rental survey data by zone and bedroom type"
+        columns:
+          - name: id
+            description: "Primary key"
+          - name: date_key
+            description: "Foreign key to dim_time"
+          - name: zone_key
+            description: "Foreign key to dim_cmhc_zone"
+
+      - name: dim_time
+        description: "Time dimension (monthly grain)"
+        columns:
+          - name: date_key
+            description: "Primary key (YYYYMMDD format)"
+
+      - name: dim_trreb_district
+        description: "TRREB district dimension with geometry"
+        columns:
+          - name: district_key
+            description: "Primary key"
+          - name: district_code
+            description: "TRREB district code"
+
+      - name: dim_cmhc_zone
+        description: "CMHC zone dimension with geometry"
+        columns:
+          - name: zone_key
+            description: "Primary key"
+          - name: zone_code
+            description: "CMHC zone code"
+
+      - name: dim_neighbourhood
+        description: "City of Toronto neighbourhoods (reference only)"
+        columns:
+          - name: neighbourhood_id
+            description: "Primary key"
+
+      - name: dim_policy_event
+        description: "Housing policy events for annotation"
+        columns:
+          - name: event_id
+            description: "Primary key"

dbt/models/staging/_staging.yml

@@ -0,0 +1,73 @@
+version: 2
+
+models:
+  - name: stg_trreb__purchases
+    description: "Staged TRREB purchase/sales data from fact_purchases"
+    columns:
+      - name: purchase_id
+        description: "Unique identifier for purchase record"
+        tests:
+          - unique
+          - not_null
+      - name: date_key
+        description: "Date dimension key (YYYYMMDD)"
+        tests:
+          - not_null
+      - name: district_key
+        description: "TRREB district dimension key"
+        tests:
+          - not_null
+
+  - name: stg_cmhc__rentals
+    description: "Staged CMHC rental market data from fact_rentals"
+    columns:
+      - name: rental_id
+        description: "Unique identifier for rental record"
+        tests:
+          - unique
+          - not_null
+      - name: date_key
+        description: "Date dimension key (YYYYMMDD)"
+        tests:
+          - not_null
+      - name: zone_key
+        description: "CMHC zone dimension key"
+        tests:
+          - not_null
+
+  - name: stg_dimensions__time
+    description: "Staged time dimension"
+    columns:
+      - name: date_key
+        description: "Date dimension key (YYYYMMDD)"
+        tests:
+          - unique
+          - not_null
+
+  - name: stg_dimensions__trreb_districts
+    description: "Staged TRREB district dimension"
+    columns:
+      - name: district_key
+        description: "District dimension key"
+        tests:
+          - unique
+          - not_null
+      - name: district_code
+        description: "TRREB district code (e.g., W01, C01)"
+        tests:
+          - unique
+          - not_null
+
+  - name: stg_dimensions__cmhc_zones
+    description: "Staged CMHC zone dimension"
+    columns:
+      - name: zone_key
+        description: "Zone dimension key"
+        tests:
+          - unique
+          - not_null
+      - name: zone_code
+        description: "CMHC zone code"
+        tests:
+          - unique
+          - not_null

dbt/models/staging/stg_cmhc__rentals.sql

@@ -0,0 +1,26 @@
+-- Staged CMHC rental market survey data
+-- Source: fact_rentals table loaded from CMHC CSV exports
+-- Grain: One row per zone per bedroom type per survey year
+
+with source as (
+    select * from {{ source('toronto_housing', 'fact_rentals') }}
+),
+
+staged as (
+    select
+        id as rental_id,
+        date_key,
+        zone_key,
+        bedroom_type,
+        universe as rental_universe,
+        avg_rent,
+        median_rent,
+        vacancy_rate,
+        availability_rate,
+        turnover_rate,
+        rent_change_pct as year_over_year_rent_change,
+        reliability_code
+    from source
+)
+
+select * from staged

dbt/models/staging/stg_dimensions__cmhc_zones.sql

@@ -0,0 +1,18 @@
+-- Staged CMHC zone dimension
+-- Source: dim_cmhc_zone table
+-- Grain: One row per zone
+
+with source as (
+    select * from {{ source('toronto_housing', 'dim_cmhc_zone') }}
+),
+
+staged as (
+    select
+        zone_key,
+        zone_code,
+        zone_name,
+        geometry
+    from source
+)
+
+select * from staged

dbt/models/staging/stg_dimensions__time.sql

@@ -0,0 +1,21 @@
+-- Staged time dimension
+-- Source: dim_time table
+-- Grain: One row per month
+
+with source as (
+    select * from {{ source('toronto_housing', 'dim_time') }}
+),
+
+staged as (
+    select
+        date_key,
+        full_date,
+        year,
+        month,
+        quarter,
+        month_name,
+        is_month_start
+    from source
+)
+
+select * from staged

dbt/models/staging/stg_dimensions__trreb_districts.sql

@@ -0,0 +1,19 @@
+-- Staged TRREB district dimension
+-- Source: dim_trreb_district table
+-- Grain: One row per district
+
+with source as (
+    select * from {{ source('toronto_housing', 'dim_trreb_district') }}
+),
+
+staged as (
+    select
+        district_key,
+        district_code,
+        district_name,
+        area_type,
+        geometry
+    from source
+)
+
+select * from staged

dbt/models/staging/stg_trreb__purchases.sql

@@ -0,0 +1,25 @@
+-- Staged TRREB purchase/sales data
+-- Source: fact_purchases table loaded from TRREB Market Watch PDFs
+-- Grain: One row per district per month
+
+with source as (
+    select * from {{ source('toronto_housing', 'fact_purchases') }}
+),
+
+staged as (
+    select
+        id as purchase_id,
+        date_key,
+        district_key,
+        sales_count,
+        dollar_volume,
+        avg_price,
+        median_price,
+        new_listings,
+        active_listings,
+        avg_dom as days_on_market,
+        avg_sp_lp as sale_to_list_ratio
+    from source
+)
+
+select * from staged

dbt/packages.yml

@@ -0,0 +1,5 @@
+packages:
+  - package: dbt-labs/dbt_utils
+    version: ">=1.0.0"
+  - package: calogica/dbt_expectations
+    version: ">=0.10.0"

dbt/profiles.yml.example

@@ -0,0 +1,21 @@
+toronto_housing:
+  target: dev
+  outputs:
+    dev:
+      type: postgres
+      host: localhost
+      user: portfolio
+      password: "{{ env_var('POSTGRES_PASSWORD') }}"
+      port: 5432
+      dbname: portfolio
+      schema: public
+      threads: 4
+    prod:
+      type: postgres
+      host: "{{ env_var('POSTGRES_HOST') }}"
+      user: "{{ env_var('POSTGRES_USER') }}"
+      password: "{{ env_var('POSTGRES_PASSWORD') }}"
+      port: 5432
+      dbname: portfolio
+      schema: public
+      threads: 4

docker-compose.yml

@@ -0,0 +1,22 @@
+services:
+  db:
+    image: postgis/postgis:16-3.4
+    container_name: portfolio-db
+    restart: unless-stopped
+    ports:
+      - "5432:5432"
+    environment:
+      POSTGRES_USER: ${POSTGRES_USER:-portfolio}
+      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-portfolio_dev}
+      POSTGRES_DB: ${POSTGRES_DB:-portfolio}
+    volumes:
+      - postgres_data:/var/lib/postgresql/data
+      - ./scripts/db/init-postgis.sql:/docker-entrypoint-initdb.d/init-postgis.sql:ro
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER:-portfolio} -d ${POSTGRES_DB:-portfolio}"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+
+volumes:
+  postgres_data:

docs/PROJECT_REFERENCE.md

@@ -0,0 +1,396 @@
+# Portfolio Project Reference
+
+**Project**: Analytics Portfolio
+**Owner**: Leo
+**Status**: Ready for Sprint 1
+
+---
+
+## Project Overview
+
+Two-project analytics portfolio demonstrating end-to-end data engineering, visualization, and ML capabilities.
+
+| Project | Domain | Key Skills | Phase |
+|---------|--------|------------|-------|
+| **Toronto Housing Dashboard** | Real estate | ETL, dimensional modeling, geospatial, choropleth | Phase 1 (Active) |
+| **Energy Pricing Analysis** | Utility markets | Time series, ML prediction, API integration | Phase 3 (Future) |
+
+**Platform**: Monolithic Dash application on self-hosted VPS (bio landing page + dashboards).
+
+---
+
+## Branching Strategy
+
+| Branch | Purpose | Deploys To |
+|--------|---------|------------|
+| `main` | Production releases only | VPS (production) |
+| `staging` | Pre-production testing | VPS (staging) |
+| `development` | Active development | Local only |
+
+**Rules**:
+- All feature branches created FROM `development`
+- All feature branches merge INTO `development`
+- `development` → `staging` for testing
+- `staging` → `main` for release
+- Direct commits to `main` or `staging` are forbidden
+- Branch naming: `feature/{sprint}-{description}` or `fix/{issue-id}`
+
+---
+
+## Tech Stack (Locked)
+
+| Layer | Technology | Version |
+|-------|------------|---------|
+| Database | PostgreSQL + PostGIS | 16.x |
+| Validation | Pydantic | ≥2.0 |
+| ORM | SQLAlchemy | ≥2.0 (2.0-style API only) |
+| Transformation | dbt-postgres | ≥1.7 |
+| Data Processing | Pandas | ≥2.1 |
+| Geospatial | GeoPandas + Shapely | ≥0.14 |
+| Visualization | Dash + Plotly | ≥2.14 |
+| UI Components | dash-mantine-components | Latest stable |
+| Testing | pytest | ≥7.0 |
+| Python | 3.11+ | Via pyenv |
+
+**Compatibility Notes**:
+- SQLAlchemy 2.0 + Pydantic 2.0 integrate well—never mix 1.x APIs
+- PostGIS extension required—enable during db init
+- Docker Compose V2 (no `version` field in compose files)
+
+---
+
+## Code Conventions
+
+### Import Style
+
+| Context | Style | Example |
+|---------|-------|---------|
+| Same directory | Single dot | `from .trreb import TRREBParser` |
+| Sibling directory | Double dot | `from ..schemas.trreb import TRREBRecord` |
+| External packages | Absolute | `import pandas as pd` |
+
+### Module Separation
+
+| Directory | Contains | Purpose |
+|-----------|----------|---------|
+| `schemas/` | Pydantic models | Data validation |
+| `models/` | SQLAlchemy ORM | Database persistence |
+| `parsers/` | PDF/CSV extraction | Raw data ingestion |
+| `loaders/` | Database operations | Data loading |
+| `figures/` | Chart factories | Plotly figure generation |
+| `callbacks/` | Dash callbacks | Per-dashboard, in `pages/{dashboard}/callbacks/` |
+| `errors/` | Exceptions + handlers | Error handling |
+
+### Code Standards
+
+- **Type hints**: Mandatory, Python 3.10+ style (`list[str]`, `dict[str, int]`, `X | None`)
+- **Functions**: Single responsibility, verb naming, early returns over nesting
+- **Docstrings**: Google style, minimal—only for non-obvious behavior
+- **Constants**: Module-level for magic values, Pydantic BaseSettings for runtime config
+
+### Error Handling
+
+```python
+# errors/exceptions.py
+class PortfolioError(Exception):
+    """Base exception."""
+
+class ParseError(PortfolioError):
+    """PDF/CSV parsing failed."""
+
+class ValidationError(PortfolioError):
+    """Pydantic or business rule validation failed."""
+
+class LoadError(PortfolioError):
+    """Database load operation failed."""
+```
+
+- Decorators for infrastructure concerns (logging, retry, transactions)
+- Explicit handling for domain logic (business rules, recovery strategies)
+
+---
+
+## Application Architecture
+
+### Dash Pages Structure
+
+```
+portfolio_app/
+├── app.py                    # Dash app factory with Pages routing
+├── config.py                 # Pydantic BaseSettings
+├── assets/                   # CSS, images (auto-served by Dash)
+├── pages/
+│   ├── home.py              # Bio landing page → /
+│   ├── toronto/
+│   │   ├── dashboard.py     # Layout only → /toronto
+│   │   └── callbacks/       # Interaction logic
+│   └── energy/              # Phase 3
+├── components/              # Shared UI (navbar, footer, cards)
+├── figures/                 # Shared chart factories
+├── toronto/                 # Toronto data logic
+│   ├── parsers/
+│   ├── loaders/
+│   ├── schemas/             # Pydantic
+│   └── models/              # SQLAlchemy
+└── errors/
+```
+
+### URL Routing (Automatic)
+
+| URL | Page | Status |
+|-----|------|--------|
+| `/` | Bio landing page | Sprint 2 |
+| `/toronto` | Toronto Housing Dashboard | Sprint 6 |
+| `/energy` | Energy Pricing Dashboard | Phase 3 |
+
+---
+
+## Phase 1: Toronto Housing Dashboard
+
+### Data Sources
+
+| Track | Source | Format | Geography | Frequency |
+|-------|--------|--------|-----------|-----------|
+| Purchases | TRREB Monthly Reports | PDF | ~35 Districts | Monthly |
+| Rentals | CMHC Rental Market Survey | CSV | ~20 Zones | Annual |
+| Enrichment | City of Toronto Open Data | GeoJSON/CSV | 158 Neighbourhoods | Census |
+| Policy Events | Curated list | CSV | N/A | Event-based |
+
+### Geographic Reality
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│ City of Toronto Neighbourhoods (158)                            │ ← Enrichment only
+├─────────────────────────────────────────────────────────────────┤
+│ TRREB Districts (~35) — W01, C01, E01, etc.                     │ ← Purchase data
+├─────────────────────────────────────────────────────────────────┤
+│ CMHC Zones (~20) — Census Tract aligned                         │ ← Rental data
+└─────────────────────────────────────────────────────────────────┘
+```
+
+**Critical**: These geographies do NOT align. Display as separate layers with toggle—do not force crosswalks.
+
+### Data Model (Star Schema)
+
+| Table | Type | Keys |
+|-------|------|------|
+| `fact_purchases` | Fact | → dim_time, dim_trreb_district |
+| `fact_rentals` | Fact | → dim_time, dim_cmhc_zone |
+| `dim_time` | Dimension | date_key (PK) |
+| `dim_trreb_district` | Dimension | district_key (PK), geometry |
+| `dim_cmhc_zone` | Dimension | zone_key (PK), geometry |
+| `dim_neighbourhood` | Dimension | neighbourhood_id (PK), geometry |
+| `dim_policy_event` | Dimension | event_id (PK) |
+
+**V1 Rule**: `dim_neighbourhood` has NO FK to fact tables—reference overlay only.
+
+### dbt Layer Structure
+
+| Layer | Naming | Purpose |
+|-------|--------|---------|
+| Staging | `stg_{source}__{entity}` | 1:1 source, cleaned, typed |
+| Intermediate | `int_{domain}__{transform}` | Business logic, filtering |
+| Marts | `mart_{domain}` | Final analytical tables |
+
+---
+
+## Sprint Overview
+
+| Sprint | Focus | Milestone |
+|--------|-------|-----------|
+| 1 | Project bootstrap, start TRREB digitization | — |
+| 2 | Bio page, data acquisition | **Launch 1: Bio Live** |
+| 3 | Parsers, schemas, models | — |
+| 4 | Loaders, dbt | — |
+| 5 | Visualization | — |
+| 6 | Polish, deploy dashboard | **Launch 2: Dashboard Live** |
+| 7 | Buffer | — |
+
+### Sprint 1 Deliverables
+
+| Category | Tasks |
+|----------|-------|
+| **Bootstrap** | Git init, pyproject.toml, .env.example, Makefile, CLAUDE.md |
+| **Infrastructure** | Docker Compose (PostgreSQL + PostGIS), scripts/ directory |
+| **App Foundation** | portfolio_app/ structure, config.py, error handling |
+| **Tests** | tests/ directory, conftest.py, pytest config |
+| **Data Acquisition** | Download TRREB PDFs, START boundary digitization (HUMAN task) |
+
+### Human Tasks (Cannot Automate)
+
+| Task | Tool | Effort |
+|------|------|--------|
+| Digitize TRREB district boundaries | QGIS | 3-4 hours |
+| Research policy events (10-20) | Manual research | 2-3 hours |
+| Replace social link placeholders | Manual | 5 minutes |
+
+---
+
+## Scope Boundaries
+
+### Phase 1 — Build These
+
+- Bio landing page with content from bio_content_v2.md
+- TRREB PDF parser
+- CMHC CSV processor
+- PostgreSQL + PostGIS database layer
+- Star schema (facts + dimensions)
+- dbt models with tests
+- Choropleth visualization (Dash)
+- Policy event annotation layer
+- Neighbourhood overlay (toggle-able)
+
+### Phase 1 — Do NOT Build
+
+| Feature | Reason | When |
+|---------|--------|------|
+| `bridge_district_neighbourhood` table | Area-weighted aggregation is Phase 4 | After Energy project |
+| Crime data integration | Deferred scope | Phase 4 |
+| Historical boundary reconciliation (140→158) | 2021+ data only for V1 | Phase 4 |
+| ML prediction models | Energy project scope | Phase 3 |
+| Multi-project shared infrastructure | Build first, abstract second | Phase 2 |
+
+If a task seems to require Phase 3/4 features, **stop and flag it**.
+
+---
+
+## File Structure
+
+### Root-Level Files (Allowed)
+
+| File | Purpose |
+|------|---------|
+| `README.md` | Project overview |
+| `CLAUDE.md` | AI assistant context |
+| `pyproject.toml` | Python packaging |
+| `.gitignore` | Git ignore rules |
+| `.env.example` | Environment template |
+| `.python-version` | pyenv version |
+| `.pre-commit-config.yaml` | Pre-commit hooks |
+| `docker-compose.yml` | Container orchestration |
+| `Makefile` | Task automation |
+
+### Directory Structure
+
+```
+portfolio/
+├── portfolio_app/           # Monolithic Dash application
+│   ├── app.py
+│   ├── config.py
+│   ├── assets/
+│   ├── pages/
+│   ├── components/
+│   ├── figures/
+│   ├── toronto/
+│   └── errors/
+├── tests/
+├── dbt/
+├── data/
+│   └── toronto/
+│       ├── raw/
+│       ├── processed/       # gitignored
+│       └── reference/
+├── scripts/
+│   ├── db/
+│   ├── docker/
+│   ├── deploy/
+│   ├── dbt/
+│   └── dev/
+├── docs/
+├── notebooks/
+├── backups/                 # gitignored
+└── reports/                 # gitignored
+```
+
+### Gitignored Directories
+
+- `data/*/processed/`
+- `reports/`
+- `backups/`
+- `notebooks/*.html`
+- `.env`
+- `__pycache__/`
+- `.venv/`
+
+---
+
+## Makefile Targets
+
+| Target | Purpose |
+|--------|---------|
+| `setup` | Install deps, create .env, init pre-commit |
+| `docker-up` | Start PostgreSQL + PostGIS |
+| `docker-down` | Stop containers |
+| `db-init` | Initialize database schema |
+| `run` | Start Dash dev server |
+| `test` | Run pytest |
+| `dbt-run` | Run dbt models |
+| `dbt-test` | Run dbt tests |
+| `lint` | Run ruff linter |
+| `format` | Run ruff formatter |
+| `ci` | Run all checks |
+| `deploy` | Deploy to production |
+
+---
+
+## Script Standards
+
+All scripts in `scripts/`:
+- Include usage comments at top
+- Idempotent where possible
+- Exit codes: 0 = success, 1 = error
+- Use `set -euo pipefail` for bash
+- Log to stdout, errors to stderr
+
+---
+
+## Environment Variables
+
+Required in `.env`:
+
+```bash
+DATABASE_URL=postgresql://user:pass@localhost:5432/portfolio
+POSTGRES_USER=portfolio
+POSTGRES_PASSWORD=<secure>
+POSTGRES_DB=portfolio
+DASH_DEBUG=true
+SECRET_KEY=<random>
+LOG_LEVEL=INFO
+```
+
+---
+
+## Success Criteria
+
+### Launch 1 (Sprint 2)
+- [ ] Bio page accessible via HTTPS
+- [ ] All bio content rendered (from bio_content_v2.md)
+- [ ] No placeholder text visible
+- [ ] Mobile responsive
+- [ ] Social links functional
+
+### Launch 2 (Sprint 6)
+- [ ] Choropleth renders TRREB districts and CMHC zones
+- [ ] Purchase/rental mode toggle works
+- [ ] Time navigation works
+- [ ] Policy event markers visible
+- [ ] Neighbourhood overlay toggleable
+- [ ] Methodology documentation published
+- [ ] Data sources cited
+
+---
+
+## Reference Documents
+
+For detailed specifications, see:
+
+| Document | Location | Use When |
+|----------|----------|----------|
+| Data schemas | `docs/toronto_housing_spec.md` | Parser/model tasks |
+| WBS details | `docs/wbs.md` | Sprint planning |
+| Bio content | `docs/bio_content.md` | Building home.py |
+
+---
+
+*Reference Version: 1.0*
+*Created: January 2026*

docs/bio_content_v2.md

@@ -0,0 +1,134 @@
+# Portfolio Bio Content
+
+**Version**: 2.0
+**Last Updated**: January 2026
+**Purpose**: Content source for `portfolio_app/pages/home.py`
+
+---
+
+## Document Context
+
+| Attribute | Value |
+|-----------|-------|
+| **Parent Document** | `portfolio_project_plan_v5.md` |
+| **Role** | Bio content and social links for landing page |
+| **Consumed By** | `portfolio_app/pages/home.py` |
+
+---
+
+## Headline
+
+**Primary**: Leo | Data Engineer & Analytics Developer
+
+**Tagline**: I build data infrastructure that actually gets used.
+
+---
+
+## Professional Summary
+
+Over the past 5 years, I've designed and evolved an enterprise analytics platform from scratch—now processing 1B+ rows across 21 tables with Python-based ETL pipelines and dbt-style SQL transformations. The result: 40% efficiency gains, 30% reduction in call abandon rates, and dashboards that executives actually open.
+
+My approach: dimensional modeling (star schema), layered transformations (staging → intermediate → marts), and automation that eliminates manual work. I've built everything from self-service analytics portals to OCR-powered receipt processing systems.
+
+Currently at Summitt Energy supporting multi-market operations across Canada and 8 US states. Previously cut my teeth on IT infrastructure projects at Petrobras (Fortune 500) and the Project Management Institute.
+
+---
+
+## Tech Stack
+
+| Category | Technologies |
+|----------|--------------|
+| **Languages** | Python, SQL |
+| **Data Processing** | Pandas, SQLAlchemy, FastAPI |
+| **Databases** | PostgreSQL, MSSQL |
+| **Visualization** | Power BI, Plotly, Dash |
+| **Patterns** | dbt, dimensional modeling, star schema |
+| **Other** | Genesys Cloud |
+
+**Display Format** (for landing page):
+```
+Python (Pandas, SQLAlchemy, FastAPI) • SQL (MSSQL, PostgreSQL) • Power BI • Plotly/Dash • Genesys Cloud • dbt patterns
+```
+
+---
+
+## Side Project
+
+**Bandit Labs** — Building automation and AI tooling for small businesses.
+
+*Note: Keep this brief on portfolio; link only if separate landing page exists.*
+
+---
+
+## Social Links
+
+| Platform | URL | Icon |
+|----------|-----|------|
+| **LinkedIn** | `https://linkedin.com/in/[USERNAME]` | `lucide-react: Linkedin` |
+| **GitHub** | `https://github.com/[USERNAME]` | `lucide-react: Github` |
+
+> **TODO**: Replace `[USERNAME]` placeholders with actual URLs before bio page launch.
+
+---
+
+## Availability Statement
+
+Open to **Senior Data Analyst**, **Analytics Engineer**, and **BI Developer** opportunities in Toronto or remote.
+
+---
+
+## Portfolio Projects Section
+
+*Dynamically populated based on deployed projects.*
+
+| Project | Status | Link |
+|---------|--------|------|
+| Toronto Housing Dashboard | In Development | `/toronto` |
+| Energy Pricing Analysis | Planned | `/energy` |
+
+**Display Logic**:
+- Show only projects with `status = deployed`
+- "In Development" projects can show as coming soon or be hidden (user preference)
+
+---
+
+## Implementation Notes
+
+### Content Hierarchy for `home.py`
+
+```
+1. Name + Tagline (hero section)
+2. Professional Summary (2-3 paragraphs)
+3. Tech Stack (horizontal chips or inline list)
+4. Portfolio Projects (cards linking to dashboards)
+5. Social Links (icon buttons)
+6. Availability statement (subtle, bottom)
+```
+
+### Styling Recommendations
+
+- Clean, minimal — let the projects speak
+- Dark/light mode support via dash-mantine-components theme
+- No headshot required (optional)
+- Mobile-responsive layout
+
+### Content Updates
+
+When updating bio content:
+1. Edit this document
+2. Update `home.py` to reflect changes
+3. Redeploy
+
+---
+
+## Related Documents
+
+| Document | Relationship |
+|----------|--------------|
+| `portfolio_project_plan_v5.md` | Parent — references this for bio content |
+| `portfolio_app/pages/home.py` | Consumer — implements this content |
+
+---
+
+*Document Version: 2.0*
+*Updated: January 2026*

docs/toronto_housing_dashboard_spec_v5.md

@@ -0,0 +1,809 @@
+# Toronto Housing Price Dashboard
+## Portfolio Project — Data Specification & Architecture
+
+**Version**: 5.1
+**Last Updated**: January 2026
+**Status**: Specification Complete
+
+---
+
+## Document Context
+
+| Attribute | Value |
+|-----------|-------|
+| **Parent Document** | `portfolio_project_plan_v5.md` |
+| **Role** | Detailed specification for Toronto Housing Dashboard |
+| **Scope** | Data schemas, source URLs, geographic boundaries, V1/V2 decisions |
+
+**Rule**: For overall project scope, phasing, tech stack, and deployment architecture, see `portfolio_project_plan_v5.md`. This document provides implementation-level detail for the Toronto Housing project specifically.
+
+**Terminology Note**: This document uses **Stages 1–4** to describe Toronto Housing implementation steps. These are distinct from the **Phases 1–5** in `portfolio_project_plan_v5.md`, which describe the overall portfolio project lifecycle.
+
+---
+
+## Project Overview
+
+A dashboard analyzing housing price variations across Toronto neighbourhoods over time, with dual analysis tracks:
+
+| Track | Data Domain | Primary Source | Geographic Unit |
+|-------|-------------|----------------|-----------------|
+| **Purchases** | Sales transactions | TRREB Monthly Reports | ~35 Districts |
+| **Rentals** | Rental market stats | CMHC Rental Market Survey | ~20 Zones |
+
+**Core Visualization**: Interactive choropleth map of Toronto with toggle between rental/purchase analysis, time-series exploration by month/year.
+
+**Enrichment Layer** (V1: overlay only): Neighbourhood-level demographic and socioeconomic context including population density, education attainment, and income. Crime data deferred to Phase 4 of the portfolio project (post-Energy project).
+
+**Tech Stack & Deployment**: See `portfolio_project_plan_v5.md` → Tech Stack, Deployment Architecture
+
+---
+
+## Geographic Layers
+
+### Layer Architecture
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│ City of Toronto Official Neighbourhoods (158) │ ← Reference overlay + Enrichment data
+├─────────────────────────────────────────────────────────────────┤
+│ TRREB Districts (~35) — W01, C01, E01, etc. │ ← Purchase data
+├─────────────────────────────────────────────────────────────────┤
+│ CMHC Survey Zones (~20) — Census Tract aligned │ ← Rental data
+└─────────────────────────────────────────────────────────────────┘
+```
+
+### Boundary Files
+
+| Layer | Zones | Format | Source | Status |
+|-------|-------|--------|--------|--------|
+| **City Neighbourhoods** | 158 | GeoJSON, Shapefile | [GitHub - jasonicarter/toronto-geojson](https://github.com/jasonicarter/toronto-geojson) | ✅ Ready to use |
+| **TRREB Districts** | ~35 | PDF only | [TRREB Toronto Map PDF](https://webapp.proptx.ca/trrebdata/common/maps/Toronto.pdf) | ⚠ Requires manual digitization |
+| **CMHC Zones** | ~20 | R package | R `cmhc` package via `get_cmhc_geography()` | ✅ Available (see note) |
+
+### Digitization Task: TRREB Districts
+
+**Input**: TRREB Toronto PDF map
+**Output**: GeoJSON with district codes (W01-W10, C01-C15, E01-E11)
+**Tool**: QGIS
+
+**Process**:
+1. Import PDF as raster layer in QGIS
+2. Create vector layer with polygon features
+3. Trace district boundaries
+4. Add attributes: `district_code`, `district_name`, `area_type` (West/Central/East)
+5. Export as GeoJSON (WGS84 / EPSG:4326)
+
+### CMHC Zone Boundaries
+
+**Source**: The R `cmhc` package provides CMHC survey geography via the `get_cmhc_geography()` function.
+
+**Extraction Process**:
+```r
+# In R
+library(cmhc)
+library(sf)
+
+# Get Toronto CMA zones
+toronto_zones <- get_cmhc_geography(
+geography_type = "ZONE",
+cma = "Toronto"
+)
+
+# Export to GeoJSON for Python/PostGIS
+st_write(toronto_zones, "cmhc_zones.geojson", driver = "GeoJSON")
+```
+
+**Output**: `data/toronto/raw/geo/cmhc_zones.geojson`
+
+**Why R?**: CMHC zone boundaries are not published as standalone files. The `cmhc` R package is the only reliable programmatic source. One-time extraction, then use GeoJSON in Python stack.
+
+### ⚠ Neighbourhood Boundary Change (140 → 158)
+
+The City of Toronto updated from 140 to 158 social planning neighbourhoods in **April 2021**. This affects data alignment:
+
+| Data Source | Pre-2021 | Post-2021 | Handling |
+|-------------|----------|-----------|----------|
+| Census (2016 and earlier) | 140 neighbourhoods | N/A | Use 140-model files |
+| Census (2021+) | N/A | 158 neighbourhoods | Use 158-model files |
+
+**V1 Strategy**: Use 2021 Census on 158 boundaries only. Defer historical trend analysis to portfolio Phase 4.
+
+---
+
+## Data Source #1: TRREB Monthly Market Reports
+
+### Source Details
+
+| Attribute | Value |
+|-----------|-------|
+| **Provider** | Toronto Regional Real Estate Board |
+| **URL** | [TRREB Market Watch](https://trreb.ca/index.php/market-news/market-watch) |
+| **Format** | PDF (monthly reports) |
+| **Update Frequency** | Monthly |
+| **Historical Availability** | 2007–Present |
+| **Access** | Public (aggregate data in PDFs) |
+| **Extraction Method** | PDF parsing (`pdfplumber` or `camelot-py`) |
+
+### Available Tables
+
+#### Table: `trreb_monthly_summary`
+**Location in PDF**: Pages 3-4 (Summary by Area)
+
+| Column | Data Type | Description |
+|--------|-----------|-------------|
+| `report_date` | DATE | First of month (YYYY-MM-01) |
+| `area_code` | VARCHAR(3) | District code (W01, C01, E01, etc.) |
+| `area_name` | VARCHAR(100) | District name |
+| `area_type` | VARCHAR(10) | West / Central / East / North |
+| `sales` | INTEGER | Number of transactions |
+| `dollar_volume` | DECIMAL | Total sales volume ($) |
+| `avg_price` | DECIMAL | Average sale price ($) |
+| `median_price` | DECIMAL | Median sale price ($) |
+| `new_listings` | INTEGER | New listings count |
+| `active_listings` | INTEGER | Active listings at month end |
+| `avg_sp_lp` | DECIMAL | Avg sale price / list price ratio (%) |
+| `avg_dom` | INTEGER | Average days on market |
+
+### Dimensions
+
+| Dimension | Granularity | Values |
+|-----------|-------------|--------|
+| **Time** | Monthly | 2007-01 to present |
+| **Geography** | District | ~35 TRREB districts |
+| **Property Type** | Aggregate | All residential (no breakdown in summary) |
+
+### Metrics Available
+
+| Metric | Aggregation | Use Case |
+|--------|-------------|----------|
+| `avg_price` | Pre-calculated monthly avg | Primary price indicator |
+| `median_price` | Pre-calculated monthly median | Robust price indicator |
+| `sales` | Count | Market activity volume |
+| `avg_dom` | Average | Market velocity |
+| `avg_sp_lp` | Ratio | Buyer/seller market indicator |
+| `new_listings` | Count | Supply indicator |
+| `active_listings` | Snapshot | Inventory level |
+
+### ⚠ Limitations
+
+- No transaction-level data (aggregates only)
+- Property type breakdown requires parsing additional tables
+- PDF structure may vary slightly across years
+- District boundaries haven't changed since 2011
+
+---
+
+## Data Source #2: CMHC Rental Market Survey
+
+### Source Details
+
+| Attribute | Value |
+|-----------|-------|
+| **Provider** | Canada Mortgage and Housing Corporation |
+| **URL** | [CMHC Housing Market Information Portal](https://www03.cmhc-schl.gc.ca/hmip-pimh/) |
+| **Format** | CSV export, API |
+| **Update Frequency** | Annual (October survey) |
+| **Historical Availability** | 1990–Present |
+| **Access** | Public, free registration for bulk downloads |
+| **Geographic Levels** | CMA → Zone → Neighbourhood → Census Tract |
+
+### Available Tables
+
+#### Table: `cmhc_rental_summary`
+**Portal Path**: Toronto → Primary Rental Market → Summary Statistics
+
+| Column | Data Type | Description |
+|--------|-----------|-------------|
+| `survey_year` | INTEGER | Survey year (October) |
+| `zone_code` | VARCHAR(10) | CMHC zone identifier |
+| `zone_name` | VARCHAR(100) | Zone name |
+| `bedroom_type` | VARCHAR(20) | Bachelor / 1-Bed / 2-Bed / 3-Bed+ / Total |
+| `universe` | INTEGER | Total rental units in zone |
+| `vacancy_rate` | DECIMAL | Vacancy rate (%) |
+| `vacancy_rate_reliability` | VARCHAR(1) | Reliability code (a/b/c/d) |
+| `availability_rate` | DECIMAL | Availability rate (%) |
+| `average_rent` | DECIMAL | Average monthly rent ($) |
+| `average_rent_reliability` | VARCHAR(1) | Reliability code |
+| `median_rent` | DECIMAL | Median monthly rent ($) |
+| `rent_change_pct` | DECIMAL | YoY rent change (%) |
+| `turnover_rate` | DECIMAL | Unit turnover rate (%) |
+
+### Dimensions
+
+| Dimension | Granularity | Values |
+|-----------|-------------|--------|
+| **Time** | Annual | 1990 to present (October snapshot) |
+| **Geography** | Zone | ~20 CMHC zones in Toronto CMA |
+| **Bedroom Type** | Category | Bachelor, 1-Bed, 2-Bed, 3-Bed+, Total |
+| **Structure Type** | Category | Row, Apartment (available in detailed tables) |
+
+### Metrics Available
+
+| Metric | Aggregation | Use Case |
+|--------|-------------|----------|
+| `average_rent` | Pre-calculated avg | Primary rent indicator |
+| `median_rent` | Pre-calculated median | Robust rent indicator |
+| `vacancy_rate` | Percentage | Market tightness |
+| `availability_rate` | Percentage | Supply accessibility |
+| `turnover_rate` | Percentage | Tenant mobility |
+| `rent_change_pct` | YoY % | Rent growth tracking |
+| `universe` | Count | Market size |
+
+### Reliability Codes
+
+| Code | Meaning | Coefficient of Variation |
+|------|---------|-------------------------|
+| `a` | Excellent | CV ≤ 2.5% |
+| `b` | Good | 2.5% < CV ≤ 5% |
+| `c` | Fair | 5% < CV ≤ 10% |
+| `d` | Poor (use with caution) | CV > 10% |
+| `**` | Data suppressed | Sample too small |
+
+### ⚠ Limitations
+
+- Annual only (no monthly granularity)
+- October snapshot (point-in-time)
+- Zones are larger than TRREB districts
+- Purpose-built rental only (excludes condo rentals in base survey)
+
+---
+
+## Data Source #3: City of Toronto Open Data
+
+### Source Details
+
+| Attribute | Value |
+|-----------|-------|
+| **Provider** | City of Toronto |
+| **URL** | [Toronto Open Data Portal](https://open.toronto.ca/) |
+| **Format** | GeoJSON, Shapefile, CSV |
+| **Use Case** | Reference layer, demographic enrichment |
+
+### Relevant Datasets
+
+#### Dataset: `neighbourhoods`
+
+| Column | Data Type | Description |
+|--------|-----------|-------------|
+| `area_id` | INTEGER | Neighbourhood ID (1-158) |
+| `area_name` | VARCHAR(100) | Official neighbourhood name |
+| `geometry` | POLYGON | Boundary geometry |
+
+#### Dataset: `neighbourhood_profiles` (Census-linked)
+
+| Column | Data Type | Description |
+|--------|-----------|-------------|
+| `neighbourhood_id` | INTEGER | Links to neighbourhoods |
+| `population` | INTEGER | Total population |
+| `avg_household_income` | DECIMAL | Average household income |
+| `dwelling_count` | INTEGER | Total dwellings |
+| `owner_pct` | DECIMAL | % owner-occupied |
+| `renter_pct` | DECIMAL | % renter-occupied |
+
+### Enrichment Potential
+
+Can overlay demographic context on housing data:
+- Income brackets by neighbourhood
+- Ownership vs rental ratios
+- Population density
+- Dwelling type distribution
+
+---
+
+## Data Source #4: Enrichment Data (Density, Education)
+
+### Purpose
+
+Provide socioeconomic context to housing price analysis. Enables questions like:
+- Do neighbourhoods with higher education attainment have higher prices?
+- How does population density correlate with price per square foot?
+
+### Geographic Alignment Reality
+
+**Critical constraint**: Enrichment data is available at the **158-neighbourhood** level, while core housing data sits at **TRREB districts (~35)** and **CMHC zones (~20)**. These do not align cleanly.
+
+```
+158 Neighbourhoods (fine) → Enrichment data lives here
+(no clean crosswalk)
+~35 TRREB Districts (coarse) → Purchase data lives here
+~20 CMHC Zones (coarse) → Rental data lives here
+```
+
+### Available Enrichment Datasets
+
+#### Dataset: Neighbourhood Profiles (Census)
+
+| Attribute | Value |
+|-----------|-------|
+| **Provider** | City of Toronto (via Statistics Canada Census) |
+| **URL** | [Toronto Open Data - Neighbourhood Profiles](https://open.toronto.ca/dataset/neighbourhood-profiles/) |
+| **Format** | CSV, JSON, XML, XLSX |
+| **Update Frequency** | Every 5 years (Census cycle) |
+| **Available Years** | 2001, 2006, 2011, 2016, 2021 |
+| **Geographic Unit** | 158 neighbourhoods (140 pre-2021) |
+
+**Key Variables**:
+
+| Variable | Description | Use Case |
+|----------|-------------|----------|
+| `population` | Total population | Density calculation |
+| `land_area_sqkm` | Area in square kilometers | Density calculation |
+| `pop_density_per_sqkm` | Population per km | Density metric |
+| `pct_bachelors_or_higher` | % age 25-64 with bachelor's+ | Education proxy |
+| `median_household_income` | Median total household income | Income metric |
+| `avg_household_income` | Average total household income | Income metric |
+| `pct_owner_occupied` | % owner-occupied dwellings | Tenure split |
+| `pct_renter_occupied` | % renter-occupied dwellings | Tenure split |
+
+**Download URL (2021, 158 neighbourhoods)**:
+```
+https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/6e19a90f-971c-46b3-852c-0c48c436d1fc/resource/19d4a806-7385-4889-acf2-256f1e079060/download/nbhd_2021_census_profile_full_158model.xlsx
+```
+
+### Crime Data — Deferred to Portfolio Phase 4
+
+Crime data (TPS Neighbourhood Crime Rates) is **not included in V1 scope**. It will be added in portfolio Phase 4 after the Energy Pricing project is complete.
+
+**Rationale**:
+- Crime data is socially/politically sensitive and requires careful methodology documentation
+- V1 focuses on core housing metrics and policy events
+- Deferral reduces scope creep risk
+
+**Future Reference** (Portfolio Phase 4):
+- Source: [TPS Public Safety Data Portal](https://data.torontopolice.on.ca/)
+- Dataset: Neighbourhood Crime Rates (Major Crime Indicators)
+- Geographic Unit: 158 neighbourhoods
+
+### V1 Enrichment Data Summary
+
+| Measure | Source | Geography | Frequency | Format | Status |
+|---------|--------|-----------|-----------|--------|--------|
+| **Population Density** | Neighbourhood Profiles | 158 neighbourhoods | Census (5-year) | CSV/JSON | ✅ Ready |
+| **Education Attainment** | Neighbourhood Profiles | 158 neighbourhoods | Census (5-year) | CSV/JSON | ✅ Ready |
+| **Median Income** | Neighbourhood Profiles | 158 neighbourhoods | Census (5-year) | CSV/JSON | ✅ Ready |
+| **Crime Rates (MCI)** | TPS Data Portal | 158 neighbourhoods | Annual | GeoJSON/CSV | Deferred to Portfolio Phase 4 |
+
+---
+
+## Data Source #5: Policy Events
+
+### Purpose
+
+Provide temporal context for housing price movements. Display as annotation markers on time series charts. **No causation claims** — correlation/context only.
+
+### Event Schema
+
+#### Table: `dim_policy_event`
+
+| Column | Data Type | Description |
+|--------|-----------|-------------|
+| `event_id` | INTEGER (PK) | Auto-increment primary key |
+| `event_date` | DATE | Date event was announced/occurred |
+| `effective_date` | DATE | Date policy took effect (if different) |
+| `level` | VARCHAR(20) | `federal` / `provincial` / `municipal` |
+| `category` | VARCHAR(20) | `monetary` / `tax` / `regulatory` / `supply` / `economic` |
+| `title` | VARCHAR(200) | Short event title for display |
+| `description` | TEXT | Longer description for tooltip |
+| `expected_direction` | VARCHAR(10) | `bearish` / `bullish` / `neutral` |
+| `source_url` | VARCHAR(500) | Link to official announcement/documentation |
+| `confidence` | VARCHAR(10) | `high` / `medium` / `low` |
+| `created_at` | TIMESTAMP | Record creation timestamp |
+
+### Event Tiers
+
+| Tier | Level | Category Examples | Inclusion Criteria |
+|------|-------|-------------------|-------------------|
+| **1** | Federal | BoC rate decisions, OSFI stress tests | Always include; objective, documented |
+| **1** | Provincial | Fair Housing Plan, foreign buyer tax, rent control | Always include; legislative record |
+| **2** | Municipal | Zoning reforms, development charges | Include if material impact expected |
+| **2** | Economic | COVID measures, major employer closures | Include if Toronto-specific impact |
+| **3** | Market | Major project announcements | Strict criteria; must be verifiable |
+
+### Expected Direction Values
+
+| Value | Meaning | Example |
+|-------|---------|---------|
+| `bullish` | Expected to increase prices | Rate cut, supply restriction |
+| `bearish` | Expected to decrease prices | Rate hike, foreign buyer tax |
+| `neutral` | Uncertain or mixed impact | Regulatory clarification |
+
+### ⚠ Caveats
+
+- **No causation claims**: Events are context, not explanation
+- **Lag effects**: Policy impact may not be immediate
+- **Confounding factors**: Multiple simultaneous influences
+- **Display only**: No statistical analysis in V1
+
+### Sample Events (Tier 1)
+
+| Date | Level | Category | Title | Direction |
+|------|-------|----------|-------|-----------|
+| 2017-04-20 | provincial | tax | Ontario Fair Housing Plan | bearish |
+| 2018-01-01 | federal | regulatory | OSFI B-20 Stress Test | bearish |
+| 2020-03-27 | federal | monetary | BoC Emergency Rate Cut (0.25%) | bullish |
+| 2022-03-02 | federal | monetary | BoC Rate Hike Cycle Begins | bearish |
+| 2023-06-01 | federal | tax | Federal 2-Year Foreign Buyer Ban | bearish |
+
+---
+
+## Data Integration Strategy
+
+### Temporal Alignment
+
+| Source | Native Frequency | Alignment Strategy |
+|--------|------------------|---------------------|
+| TRREB | Monthly | Use as-is |
+| CMHC | Annual (October) | Spread to monthly OR display annual overlay |
+| Census/Enrichment | 5-year | Static snapshot; display as reference |
+| Policy Events | Event-based | Display as vertical markers on time axis |
+
+**Recommendation**: Keep separate time axes. TRREB monthly for purchases, CMHC annual for rentals. Don't force artificial monthly rental data.
+
+### Geographic Alignment
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│ VISUALIZATION APPROACH │
+├─────────────────────────────────────────────────────────────────┤
+│ │
+│ Purchase Mode Rental Mode │
+│ ───────────────── ────────────── │
+│ Map: TRREB Districts Map: CMHC Zones │
+│ Time: Monthly slider Time: Annual selector │
+│ Metrics: Price, Sales Metrics: Rent, Vacancy │
+│ │
+│ ┌───────────────────────────────────────────────────────┐ │
+│ │ City Neighbourhoods Overlay │ │
+│ │ (158 boundaries as reference layer) │ │
+│ │ + Enrichment data (density, education, income) │ │
+│ ──────────────────────────────────────────────────────────┘ │
+│ │
+────────────────────────────────────────────────────────────────────┘
+```
+
+### Enrichment Integration Strategy (Phased)
+
+#### V1: Reference Overlay (Current Scope)
+
+**Approach**: Display neighbourhood enrichment as a separate toggle-able layer. No joins to housing data.
+
+**UX**:
+- User hovers over TRREB district → tooltip shows "This district contains neighbourhoods: Annex, Casa Loma, Yorkville..."
+- User toggles "Show Enrichment" → choropleth switches to neighbourhood-level density/education/income
+- Enrichment and housing metrics displayed side-by-side, not merged
+
+**Pros**:
+- No imputation or dodgy aggregations
+- Honest about geographic mismatch
+- Ships faster
+
+**Cons**:
+- Can't do correlation analysis (price vs. enrichment) directly in dashboard
+
+**Implementation**:
+- `dim_neighbourhood` as standalone dimension (no FK to fact tables)
+- Spatial lookup on hover (point-in-polygon)
+
+#### V2/Portfolio Phase 4: Area-Weighted Aggregation (Future Scope)
+
+**Approach**: Pre-compute area-weighted averages of neighbourhood metrics for each TRREB district and CMHC zone.
+
+**Process**:
+1. Spatial join: intersect neighbourhood polygons with TRREB/CMHC polygons
+2. Compute overlap area for each neighbourhood-district pair
+3. Weight neighbourhood metrics by overlap area proportion
+4. User selects aggregation method in UI
+
+**Aggregation Methods to Expose**:
+
+| Method | Description | Best For |
+|--------|-------------|----------|
+| **Area-weighted mean** | Weight by % overlap area | Continuous metrics (density) |
+| **Population-weighted mean** | Weight by population in overlap | Per-capita metrics (education) |
+| **Majority assignment** | Assign neighbourhood to district with >50% overlap | Categorical data |
+| **Max overlap** | Assign to single district with largest overlap | 1:1 mapping needs |
+
+**Default**: Population-weighted (more defensible for per-capita metrics). Hide selector behind "Advanced" toggle.
+
+### V1 Future-Proofing (Do Now)
+
+| Action | Why |
+|--------|-----|
+| Store neighbourhood boundaries in same CRS as TRREB/CMHC (WGS84) | Avoids reprojection headaches |
+| Keep `dim_neighbourhood` normalized (not denormalized into district tables) | Clean separation for V2 join |
+| Document Census year for each metric | Ready for 2026 Census |
+| Include `census_year` column in dim_neighbourhood | Enables SCD tracking |
+
+### V1 Defer (Don't Do Yet)
+
+| Action | Why Not |
+|--------|---------|
+| Pre-compute area-weighted crosswalk | Don't need for V1 |
+| Build aggregation method selector UI | No backend to support it |
+| Crime data integration | Deferred to Portfolio Phase 4 |
+| Historical neighbourhood boundary reconciliation (140→158) | Use 2021+ data only for V1 |
+
+---
+
+## Proposed Data Model
+
+### Star Schema
+
+```
+┌──────────────────┐
+│ dim_time │
+├──────────────────┤
+│ date_key (PK) │
+│ year │
+│ month │
+│ quarter │
+│ month_name │
+───────────────────────┘
+│
+┌─────────────────────────────────────────────┐
+│ │ │
+│
+┌──────────────────┐ │ ┌──────────────────┐
+│ dim_trreb_district│ │ │ dim_cmhc_zone │
+├──────────────────┤ │ ├──────────────────┤
+│ district_key (PK)│ │ │ zone_key (PK) │
+│ district_code │ │ │ zone_code │
+│ district_name │ │ │ zone_name │
+│ area_type │ │ │ geometry │
+│ geometry │
+───────────────────────┘ │ │
+│ │ │
+│
+┌──────────────────┐ │ ┌──────────────────┐
+│ fact_purchases │ │ │ fact_rentals │
+├──────────────────┤ │ ├──────────────────┤
+│ date_key (FK) │ │ │ date_key (FK) │
+│ district_key (FK)│ │ │ zone_key (FK) │
+│ sales_count │ │ │ bedroom_type │
+│ avg_price │ │ │ avg_rent │
+│ median_price │ │ │ median_rent │
+│ new_listings │ │ │ vacancy_rate │
+│ active_listings │ │ │ universe │
+│ avg_dom │ │ │ turnover_rate │
+│ avg_sp_lp │ │ │ reliability_code │
+─────────────────────┘ │ ─────────────────────┘
+│
+
+┌───────────────────────────┐
+│ dim_neighbourhood │
+├───────────────────────────┤
+│ neighbourhood_id (PK) │
+│ name │
+│ geometry │
+│ population │
+│ land_area_sqkm │
+│ pop_density_per_sqkm │
+│ pct_bachelors_or_higher │
+│ median_household_income │
+│ pct_owner_occupied │
+│ pct_renter_occupied │
+│ census_year │ ← For SCD tracking
+──────────────────────────────┘
+
+┌───────────────────────────┐
+│ dim_policy_event │
+├───────────────────────────┤
+│ event_id (PK) │
+│ event_date │
+│ effective_date │
+│ level │ ← federal/provincial/municipal
+│ category │ ← monetary/tax/regulatory/supply/economic
+│ title │
+│ description │
+│ expected_direction │ ← bearish/bullish/neutral
+│ source_url │
+│ confidence │ ← high/medium/low
+│ created_at │
+──────────────────────────────┘
+
+┌───────────────────────────┐
+│ bridge_district_neighbourhood │ ← Portfolio Phase 4 ONLY
+├───────────────────────────┤
+│ district_key (FK) │
+│ neighbourhood_id (FK) │
+│ area_overlap_pct │
+│ population_overlap │ ← For pop-weighted agg
+──────────────────────────────┘
+```
+
+**Notes**:
+- `dim_neighbourhood` has no FK relationship to fact tables in V1
+- `dim_policy_event` is standalone (no FK to facts); used for time-series annotation
+- `bridge_district_neighbourhood` is Portfolio Phase 4 scope only
+- Similar bridge table needed for CMHC zones in Portfolio Phase 4
+
+---
+
+## File Structure
+
+> **Note**: Toronto Housing data logic lives in `portfolio_app/toronto/`. See `portfolio_project_plan_v5.md` for full project structure.
+
+### Data Directory Structure
+
+```
+data/
+└── toronto/
+    ├── raw/
+    │   ├── trreb/
+    │   │   └── market_watch_YYYY_MM.pdf
+    │   ├── cmhc/
+    │   │   └── rental_survey_YYYY.csv
+    │   ├── enrichment/
+    │   │   └── neighbourhood_profiles_2021.xlsx
+    │   └── geo/
+    │       ├── toronto_neighbourhoods.geojson
+    │       ├── trreb_districts.geojson        ← (to be created via QGIS)
+    │       └── cmhc_zones.geojson             ← (from R cmhc package)
+    │
+    ├── processed/                              ← gitignored
+    │   ├── fact_purchases.parquet
+    │   ├── fact_rentals.parquet
+    │   ├── dim_time.parquet
+    │   ├── dim_trreb_district.parquet
+    │   ├── dim_cmhc_zone.parquet
+    │   ├── dim_neighbourhood.parquet
+    │   └── dim_policy_event.parquet
+    │
+    └── reference/
+        ├── policy_events.csv                   ← Curated event list
+        └── neighbourhood_boundary_changelog.md ← 140→158 notes
+```
+
+### Code Module Structure
+
+```
+portfolio_app/toronto/
+├── __init__.py
+├── parsers/
+│   ├── __init__.py
+│   ├── trreb.py                    # PDF extraction
+│   └── cmhc.py                     # CSV processing
+├── loaders/
+│   ├── __init__.py
+│   └── database.py                 # DB operations
+├── schemas/                        # Pydantic models
+│   ├── __init__.py
+│   ├── trreb.py
+│   ├── cmhc.py
+│   ├── enrichment.py
+│   └── policy_event.py
+├── models/                         # SQLAlchemy ORM
+│   ├── __init__.py
+│   ├── base.py                     # DeclarativeBase, engine
+│   ├── dimensions.py               # dim_time, dim_trreb_district, dim_policy_event, etc.
+│   └── facts.py                    # fact_purchases, fact_rentals
+└── transforms/
+    └── __init__.py
+```
+
+### Notebooks
+
+```
+notebooks/
+├── 01_trreb_pdf_extraction.ipynb
+├── 02_cmhc_data_prep.ipynb
+├── 03_geo_layer_prep.ipynb
+├── 04_enrichment_data_prep.ipynb
+├── 05_policy_events_curation.ipynb
+└── 06_spatial_crosswalk.ipynb          ← Portfolio Phase 4 only
+```
+
+---
+
+## ✅ Implementation Checklist
+
+> **Note**: These are **Stages** within the Toronto Housing project (Portfolio Phase 1). They are distinct from the overall portfolio **Phases** defined in `portfolio_project_plan_v5.md`.
+
+### Stage 1: Data Acquisition
+- [ ] Download TRREB monthly PDFs (2020-present as MVP)
+- [ ] Register for CMHC portal and export Toronto rental data
+- [ ] Extract CMHC zone boundaries via R `cmhc` package
+- [ ] Download City of Toronto neighbourhood GeoJSON (158 boundaries)
+- [ ] Digitize TRREB district boundaries in QGIS
+- [ ] Download Neighbourhood Profiles (2021 Census, 158-model)
+
+### Stage 2: Data Processing
+- [ ] Build TRREB PDF parser (`portfolio_app/toronto/parsers/trreb.py`)
+- [ ] Build Pydantic schemas (`portfolio_app/toronto/schemas/`)
+- [ ] Build SQLAlchemy models (`portfolio_app/toronto/models/`)
+- [ ] Extract and validate TRREB monthly summaries
+- [ ] Clean and structure CMHC rental data
+- [ ] Process Neighbourhood Profiles into `dim_neighbourhood`
+- [ ] Curate and load policy events into `dim_policy_event`
+- [ ] Create dimension tables
+- [ ] Build fact tables
+- [ ] Validate all geospatial layers use same CRS (WGS84/EPSG:4326)
+
+### Stage 3: Visualization (V1)
+- [ ] Create dashboard page (`portfolio_app/pages/toronto/dashboard.py`)
+- [ ] Build choropleth figures (`portfolio_app/figures/choropleth.py`)
+- [ ] Build time series figures (`portfolio_app/figures/time_series.py`)
+- [ ] Design dashboard layout (purchase/rental toggle)
+- [ ] Implement choropleth map with layer switching
+- [ ] Add time slider/selector
+- [ ] Build neighbourhood overlay (toggle-able)
+- [ ] Add enrichment layer toggle (density/education/income choropleth)
+- [ ] Add policy event markers on time series
+- [ ] Add tooltips with cross-reference info ("This district contains...")
+- [ ] Add tooltips showing enrichment metrics on hover
+
+### Stage 4: Polish (V1)
+- [ ] Add data source citations
+- [ ] Document methodology (especially geographic limitations)
+- [ ] Write docs (`docs/methodology.md`, `docs/data_sources.md`)
+- [ ] Deploy to portfolio
+
+### Future Enhancements (Portfolio Phase 4 — Post-Energy Project)
+- [ ] Add crime data to dim_neighbourhood
+- [ ] Build spatial crosswalk (neighbourhood ↔ district/zone intersections)
+- [ ] Compute area-weighted and population-weighted aggregations
+- [ ] Add aggregation method selector to UI
+- [ ] Enable correlation analysis (price vs. enrichment metrics)
+- [ ] Add historical neighbourhood boundary support (140→158)
+
+**Deployment & dbt Architecture**: See `portfolio_project_plan_v5.md` for:
+- dbt layer structure and testing strategy
+- Deployment architecture
+- Data quality framework
+
+---
+
+## References & Links
+
+### Core Housing Data
+
+| Resource | URL |
+|----------|-----|
+| TRREB Market Watch | https://trreb.ca/index.php/market-news/market-watch |
+| CMHC Housing Portal | https://www03.cmhc-schl.gc.ca/hmip-pimh/ |
+
+### Geographic Boundaries
+
+| Resource | URL |
+|----------|-----|
+| Toronto Neighbourhoods GeoJSON | https://github.com/jasonicarter/toronto-geojson |
+| TRREB District Map (PDF) | https://webapp.proptx.ca/trrebdata/common/maps/Toronto.pdf |
+| Statistics Canada Census Tracts | https://www12.statcan.gc.ca/census-recensement/2021/geo/sip-pis/boundary-limites/index-eng.cfm |
+| R `cmhc` package (CRAN) | https://cran.r-project.org/package=cmhc |
+
+### Enrichment Data
+
+| Resource | URL |
+|----------|-----|
+| Toronto Open Data Portal | https://open.toronto.ca/ |
+| Neighbourhood Profiles (CKAN) | https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/neighbourhood-profiles |
+| Neighbourhood Profiles 2021 (Direct Download) | https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/6e19a90f-971c-46b3-852c-0c48c436d1fc/resource/19d4a806-7385-4889-acf2-256f1e079060/download/nbhd_2021_census_profile_full_158model.xlsx |
+
+### Policy Events Research
+
+| Resource | URL |
+|----------|-----|
+| Bank of Canada Interest Rates | https://www.bankofcanada.ca/rates/interest-rates/ |
+| OSFI (Stress Test Rules) | https://www.osfi-bsif.gc.ca/ |
+| Ontario Legislature (Bills) | https://www.ola.org/ |
+
+### Reference Documentation
+
+| Resource | URL |
+|----------|-----|
+| Statistics Canada 2021 Census Reference | https://www12.statcan.gc.ca/census-recensement/2021/ref/index-eng.cfm |
+| City of Toronto Neighbourhood Profiles Overview | https://www.toronto.ca/city-government/data-research-maps/neighbourhoods-communities/neighbourhood-profiles/ |
+
+---
+
+## Related Documents
+
+| Document | Relationship | Use For |
+|----------|--------------|---------|
+| `portfolio_project_plan_v5.md` | Parent document | Overall scope, phasing, tech stack, deployment, dbt architecture, data quality framework |
+
+---
+
+*Document Version: 5.1*
+*Updated: January 2026*
+*Project: Toronto Housing Price Dashboard — Portfolio Piece*

docs/wbs_sprint_plan_v4.md

@@ -0,0 +1,794 @@
+# Work Breakdown Structure & Sprint Plan
+
+**Project**: Toronto Housing Dashboard (Portfolio Phase 1)
+**Version**: 4.1
+**Date**: January 2026
+
+---
+
+## Document Context
+
+| Attribute | Value |
+|-----------|-------|
+| **Parent Documents** | `portfolio_project_plan_v5.md`, `toronto_housing_dashboard_spec_v5.md` |
+| **Content Source** | `bio_content_v2.md` |
+| **Role** | Executable sprint plan for Phase 1 delivery |
+
+---
+
+## Milestones
+
+| Milestone | Deliverable | Target Sprint |
+|-----------|-------------|---------------|
+| **Launch 1** | Bio Landing Page | Sprint 2 |
+| **Launch 2** | Toronto Housing Dashboard | Sprint 6 |
+
+---
+
+## WBS Structure
+
+```
+1.0 Launch 1: Bio Landing Page
+├── 1.1 Project Bootstrap
+├── 1.2 Infrastructure
+├── 1.3 Application Foundation
+├── 1.4 Bio Page
+└── 1.5 Deployment
+
+2.0 Launch 2: Toronto Housing Dashboard
+├── 2.1 Data Acquisition
+├── 2.2 Data Processing
+├── 2.3 Database Layer
+├── 2.4 dbt Transformation
+├── 2.5 Visualization
+├── 2.6 Documentation
+└── 2.7 Operations
+```
+
+---
+
+## Launch 1: Bio Landing Page
+
+### 1.1 Project Bootstrap
+
+| ID | Task | Depends On | Effort | Complexity |
+|----|------|------------|--------|------------|
+| 1.1.1 | Git repository initialization | — | Low | Low |
+| 1.1.2 | Create `.gitignore` | 1.1.1 | Low | Low |
+| 1.1.3 | Create `pyproject.toml` | 1.1.1 | Low | Low |
+| 1.1.4 | Create `.python-version` (3.11+) | 1.1.1 | Low | Low |
+| 1.1.5 | Create `.env.example` | 1.1.1 | Low | Low |
+| 1.1.6 | Create `README.md` (initial) | 1.1.1 | Low | Low |
+| 1.1.7 | Create `CLAUDE.md` | 1.1.1 | Low | Low |
+| 1.1.8 | Create `Makefile` with all targets | 1.1.3 | Low | Medium |
+
+### 1.2 Infrastructure
+
+| ID | Task | Depends On | Effort | Complexity |
+|----|------|------------|--------|------------|
+| 1.2.1 | Python env setup (pyenv, venv, deps) | 1.1.3, 1.1.4 | Low | Low |
+| 1.2.2 | Create `.pre-commit-config.yaml` | 1.2.1 | Low | Low |
+| 1.2.3 | Install pre-commit hooks | 1.2.2 | Low | Low |
+| 1.2.4 | Create `docker-compose.yml` (PostgreSQL + PostGIS) | 1.1.5 | Low | Low |
+| 1.2.5 | Create `scripts/` directory structure | 1.1.1 | Low | Low |
+| 1.2.6 | Create `scripts/docker/up.sh` | 1.2.5 | Low | Low |
+| 1.2.7 | Create `scripts/docker/down.sh` | 1.2.5 | Low | Low |
+| 1.2.8 | Create `scripts/docker/logs.sh` | 1.2.5 | Low | Low |
+| 1.2.9 | Create `scripts/docker/rebuild.sh` | 1.2.5 | Low | Low |
+| 1.2.10 | Create `scripts/db/init.sh` (PostGIS extension) | 1.2.5 | Low | Low |
+| 1.2.11 | Create `scripts/dev/setup.sh` | 1.2.5 | Low | Low |
+| 1.2.12 | Verify Docker + PostGIS working | 1.2.4, 1.2.10 | Low | Low |
+
+### 1.3 Application Foundation
+
+| ID | Task | Depends On | Effort | Complexity |
+|----|------|------------|--------|------------|
+| 1.3.1 | Create `portfolio_app/` directory structure (full tree) | 1.2.1 | Low | Low |
+| 1.3.2 | Create `portfolio_app/__init__.py` | 1.3.1 | Low | Low |
+| 1.3.3 | Create `portfolio_app/config.py` (Pydantic BaseSettings) | 1.3.1 | Low | Medium |
+| 1.3.4 | Create `portfolio_app/errors/__init__.py` | 1.3.1 | Low | Low |
+| 1.3.5 | Create `portfolio_app/errors/exceptions.py` | 1.3.4 | Low | Low |
+| 1.3.6 | Create `portfolio_app/errors/handlers.py` | 1.3.5 | Low | Medium |
+| 1.3.7 | Create `portfolio_app/app.py` (Dash + Pages routing) | 1.3.3 | Low | Medium |
+| 1.3.8 | Configure dash-mantine-components theme | 1.3.7 | Low | Low |
+| 1.3.9 | Create `portfolio_app/assets/` directory | 1.3.1 | Low | Low |
+| 1.3.10 | Create `portfolio_app/assets/styles.css` | 1.3.9 | Low | Medium |
+| 1.3.11 | Create `portfolio_app/assets/variables.css` | 1.3.9 | Low | Low |
+| 1.3.12 | Add `portfolio_app/assets/favicon.ico` | 1.3.9 | Low | Low |
+| 1.3.13 | Create `portfolio_app/assets/images/` directory | 1.3.9 | Low | Low |
+| 1.3.14 | Create `tests/` directory structure | 1.2.1 | Low | Low |
+| 1.3.15 | Create `tests/__init__.py` | 1.3.14 | Low | Low |
+| 1.3.16 | Create `tests/conftest.py` | 1.3.14 | Low | Medium |
+| 1.3.17 | Configure pytest in `pyproject.toml` | 1.1.3, 1.3.14 | Low | Low |
+
+### 1.4 Bio Page
+
+| ID | Task | Depends On | Effort | Complexity |
+|----|------|------------|--------|------------|
+| 1.4.1 | Create `portfolio_app/components/__init__.py` | 1.3.1 | Low | Low |
+| 1.4.2 | Create `portfolio_app/components/navbar.py` | 1.4.1, 1.3.8 | Low | Low |
+| 1.4.3 | Create `portfolio_app/components/footer.py` | 1.4.1, 1.3.8 | Low | Low |
+| 1.4.4 | Create `portfolio_app/components/cards.py` | 1.4.1, 1.3.8 | Low | Low |
+| 1.4.5 | Create `portfolio_app/pages/__init__.py` | 1.3.1 | Low | Low |
+| 1.4.6 | Create `portfolio_app/pages/home.py` (layout) | 1.4.5, 1.4.2, 1.4.3 | Low | Low |
+| 1.4.7 | Integrate bio content from `bio_content_v2.md` | 1.4.6 | Low | Low |
+| 1.4.8 | Replace social link placeholders with real URLs | 1.4.7 | Low | Low |
+| 1.4.9 | Implement project cards (deployed/in-dev logic) | 1.4.4, 1.4.6 | Low | Low |
+| 1.4.10 | Test bio page renders locally | 1.4.9 | Low | Low |
+
+### 1.5 Deployment
+
+| ID | Task | Depends On | Effort | Complexity |
+|----|------|------------|--------|------------|
+| 1.5.1 | Install PostgreSQL + PostGIS on VPS | — | Low | Low |
+| 1.5.2 | Configure firewall (ufw: SSH, HTTP, HTTPS) | 1.5.1 | Low | Low |
+| 1.5.3 | Create application database user | 1.5.1 | Low | Low |
+| 1.5.4 | Create Gunicorn systemd service file | 1.4.10 | Low | Low |
+| 1.5.5 | Configure Nginx reverse proxy | 1.5.4 | Low | Low |
+| 1.5.6 | Configure SSL (certbot) | 1.5.5 | Low | Low |
+| 1.5.7 | Create `scripts/deploy/deploy.sh` | 1.2.5 | Low | Low |
+| 1.5.8 | Create `scripts/deploy/health-check.sh` | 1.2.5 | Low | Low |
+| 1.5.9 | Deploy bio page | 1.5.6, 1.5.7 | Low | Low |
+| 1.5.10 | Verify HTTPS access | 1.5.9 | Low | Low |
+
+---
+
+## Launch 2: Toronto Housing Dashboard
+
+### 2.1 Data Acquisition
+
+| ID | Task | Depends On | Effort | Complexity |
+|----|------|------------|--------|------------|
+| 2.1.1 | Define TRREB year scope + download PDFs | — | Low | Low |
+| 2.1.2 | **HUMAN**: Digitize TRREB district boundaries (QGIS) | 2.1.1 | High | High |
+| 2.1.3 | Register for CMHC portal | — | Low | Low |
+| 2.1.4 | Export CMHC Toronto rental CSVs | 2.1.3 | Low | Low |
+| 2.1.5 | Extract CMHC zone boundaries (R cmhc package) | 2.1.3 | Low | Medium |
+| 2.1.6 | Download neighbourhoods GeoJSON (158 boundaries) | — | Low | Low |
+| 2.1.7 | Download Neighbourhood Profiles 2021 (xlsx) | — | Low | Low |
+| 2.1.8 | Validate CRS alignment (all geo files WGS84) | 2.1.2, 2.1.5, 2.1.6 | Low | Medium |
+| 2.1.9 | Research Tier 1 policy events (10—20 events) | — | Mid | Medium |
+| 2.1.10 | Create `data/toronto/reference/policy_events.csv` | 2.1.9 | Low | Low |
+| 2.1.11 | Create `data/` directory structure | 1.3.1 | Low | Low |
+| 2.1.12 | Organize raw files into `data/toronto/raw/` | 2.1.11 | Low | Low |
+| 2.1.13 | Test TRREB parser across year boundaries | 2.2.3 | Low | Medium |
+
+### 2.2 Data Processing
+
+| ID | Task | Depends On | Effort | Complexity |
+|----|------|------------|--------|------------|
+| 2.2.1 | Create `portfolio_app/toronto/__init__.py` | 1.3.1 | Low | Low |
+| 2.2.2 | Create `portfolio_app/toronto/parsers/__init__.py` | 2.2.1 | Low | Low |
+| 2.2.3 | Build TRREB PDF parser (`parsers/trreb.py`) | 2.2.2, 2.1.1 | Mid | High |
+| 2.2.4 | TRREB data cleaning/normalization | 2.2.3 | Low | Medium |
+| 2.2.5 | TRREB parser unit tests | 2.2.4 | Low | Low |
+| 2.2.6 | Build CMHC CSV processor (`parsers/cmhc.py`) | 2.2.2, 2.1.4 | Low | Low |
+| 2.2.7 | CMHC reliability code handling | 2.2.6 | Low | Low |
+| 2.2.8 | CMHC processor unit tests | 2.2.7 | Low | Low |
+| 2.2.9 | Build Neighbourhood Profiles parser | 2.2.1, 2.1.7 | Low | Low |
+| 2.2.10 | Policy events CSV loader | 2.2.1, 2.1.10 | Low | Low |
+
+### 2.3 Database Layer
+
+| ID | Task | Depends On | Effort | Complexity |
+|----|------|------------|--------|------------|
+| 2.3.1 | Create `portfolio_app/toronto/schemas/__init__.py` | 2.2.1 | Low | Low |
+| 2.3.2 | Create TRREB Pydantic schemas (`schemas/trreb.py`) | 2.3.1 | Low | Medium |
+| 2.3.3 | Create CMHC Pydantic schemas (`schemas/cmhc.py`) | 2.3.1 | Low | Medium |
+| 2.3.4 | Create enrichment Pydantic schemas (`schemas/enrichment.py`) | 2.3.1 | Low | Low |
+| 2.3.5 | Create policy event Pydantic schema (`schemas/policy_event.py`) | 2.3.1 | Low | Low |
+| 2.3.6 | Create `portfolio_app/toronto/models/__init__.py` | 2.2.1 | Low | Low |
+| 2.3.7 | Create SQLAlchemy base (`models/base.py`) | 2.3.6, 1.3.3 | Low | Medium |
+| 2.3.8 | Create dimension models (`models/dimensions.py`) | 2.3.7 | Low | Medium |
+| 2.3.9 | Create fact models (`models/facts.py`) | 2.3.8 | Low | Medium |
+| 2.3.10 | Create `portfolio_app/toronto/loaders/__init__.py` | 2.2.1 | Low | Low |
+| 2.3.11 | Create dimension loaders (`loaders/database.py`) | 2.3.10, 2.3.8 | Low | Medium |
+| 2.3.12 | Create fact loaders | 2.3.11, 2.3.9, 2.2.4, 2.2.7 | Mid | Medium |
+| 2.3.13 | Loader integration tests | 2.3.12 | Low | Medium |
+| 2.3.14 | Create SQL views for dashboard queries | 2.3.12 | Low | Medium |
+
+### 2.4 dbt Transformation
+
+| ID | Task | Depends On | Effort | Complexity |
+|----|------|------------|--------|------------|
+| 2.4.1 | Create `dbt/` directory structure | 1.3.1 | Low | Low |
+| 2.4.2 | Create `dbt/dbt_project.yml` | 2.4.1 | Low | Low |
+| 2.4.3 | Create `dbt/profiles.yml` | 2.4.1, 1.3.3 | Low | Low |
+| 2.4.4 | Create `scripts/dbt/run.sh` | 1.2.5 | Low | Low |
+| 2.4.5 | Create `scripts/dbt/test.sh` | 1.2.5 | Low | Low |
+| 2.4.6 | Create `scripts/dbt/docs.sh` | 1.2.5 | Low | Low |
+| 2.4.7 | Create `scripts/dbt/fresh.sh` | 1.2.5 | Low | Low |
+| 2.4.8 | Create staging models (`stg_trreb__monthly`, `stg_cmhc__rental`) | 2.4.3, 2.3.12 | Low | Medium |
+| 2.4.9 | Create intermediate models | 2.4.8 | Low | Medium |
+| 2.4.10 | Create mart models | 2.4.9 | Low | Medium |
+| 2.4.11 | Create dbt schema tests (unique, not_null, relationships) | 2.4.10 | Low | Medium |
+| 2.4.12 | Create custom dbt tests (anomaly detection) | 2.4.11 | Low | Medium |
+| 2.4.13 | Create dbt documentation (schema.yml) | 2.4.10 | Low | Low |
+
+### 2.5 Visualization
+
+| ID | Task | Depends On | Effort | Complexity |
+|----|------|------------|--------|------------|
+| 2.5.1 | Create `portfolio_app/figures/__init__.py` | 1.3.1 | Low | Low |
+| 2.5.2 | Build choropleth factory (`figures/choropleth.py`) | 2.5.1, 2.1.8 | Mid | Medium |
+| 2.5.3 | Build time series factory (`figures/time_series.py`) | 2.5.1 | Low | Medium |
+| 2.5.4 | Build YoY change chart factory (`figures/statistical.py`) | 2.5.1 | Low | Medium |
+| 2.5.5 | Build seasonality decomposition chart | 2.5.4 | Low | Medium |
+| 2.5.6 | Build district correlation matrix chart | 2.5.4 | Low | Medium |
+| 2.5.7 | Create `portfolio_app/pages/toronto/__init__.py` | 1.4.5 | Low | Low |
+| 2.5.8 | Create `portfolio_app/pages/toronto/dashboard.py` (layout only) | 2.5.7, 1.4.2, 1.4.3 | Mid | High |
+| 2.5.9 | Implement purchase/rental mode toggle | 2.5.8 | Low | Low |
+| 2.5.10 | Implement monthly time slider | 2.5.8 | Low | Medium |
+| 2.5.11 | Implement annual time selector (CMHC) | 2.5.8 | Low | Low |
+| 2.5.12 | Implement layer toggles (districts/zones/neighbourhoods) | 2.5.8 | Low | Medium |
+| 2.5.13 | Create `portfolio_app/pages/toronto/callbacks/__init__.py` | 2.5.7 | Low | Low |
+| 2.5.14 | Create `callbacks/map_callbacks.py` | 2.5.13, 2.5.2 | Mid | Medium |
+| 2.5.15 | Create `callbacks/filter_callbacks.py` | 2.5.13 | Low | Medium |
+| 2.5.16 | Create `callbacks/timeseries_callbacks.py` | 2.5.13, 2.5.3 | Low | Medium |
+| 2.5.17 | Implement district/zone tooltips | 2.5.14 | Low | Low |
+| 2.5.18 | Implement neighbourhood overlay | 2.5.14, 2.1.6 | Low | Medium |
+| 2.5.19 | Implement enrichment layer toggle | 2.5.18 | Low | Medium |
+| 2.5.20 | Implement policy event markers on time series | 2.5.16, 2.2.10 | Low | Medium |
+| 2.5.21 | Implement "district contains neighbourhoods" tooltip | 2.5.17 | Low | Low |
+| 2.5.22 | Test dashboard renders with sample data | 2.5.20 | Low | Medium |
+
+### 2.6 Documentation
+
+| ID | Task | Depends On | Effort | Complexity |
+|----|------|------------|--------|------------|
+| 2.6.1 | Create `docs/` directory | 1.3.1 | Low | Low |
+| 2.6.2 | Write `docs/methodology.md` (geographic limitations) | 2.5.22 | Low | Medium |
+| 2.6.3 | Write `docs/data_sources.md` (citations) | 2.5.22 | Low | Low |
+| 2.6.4 | Write `docs/user_guide.md` | 2.5.22 | Low | Low |
+| 2.6.5 | Update `README.md` (final) | 2.6.2, 2.6.3 | Low | Low |
+| 2.6.6 | Update `CLAUDE.md` (final) | 2.6.5 | Low | Low |
+
+### 2.7 Operations
+
+| ID | Task | Depends On | Effort | Complexity |
+|----|------|------------|--------|------------|
+| 2.7.1 | Create `scripts/db/backup.sh` | 1.2.5 | Low | Low |
+| 2.7.2 | Create `scripts/db/restore.sh` | 1.2.5 | Low | Low |
+| 2.7.3 | Create `scripts/db/reset.sh` (dev only) | 1.2.5 | Low | Low |
+| 2.7.4 | Create `scripts/deploy/rollback.sh` | 1.2.5 | Low | Medium |
+| 2.7.5 | Implement backup retention policy | 2.7.1 | Low | Low |
+| 2.7.6 | Add `/health` endpoint | 2.5.8 | Low | Low |
+| 2.7.7 | Configure uptime monitoring (external) | 2.7.6 | Low | Low |
+| 2.7.8 | Deploy Toronto dashboard | 1.5.9, 2.5.22 | Low | Low |
+| 2.7.9 | Verify production deployment | 2.7.8 | Low | Low |
+
+---
+
+## L3 Task Details
+
+### 1.1 Project Bootstrap
+
+#### 1.1.1 Git repository initialization
+| Attribute | Value |
+|-----------|-------|
+| **What** | Initialize git repo with main branch |
+| **How** | `git init`, initial commit |
+| **Inputs** | — |
+| **Outputs** | `.git/` directory |
+| **Why** | Version control foundation |
+
+#### 1.1.2 Create `.gitignore`
+| Attribute | Value |
+|-----------|-------|
+| **What** | Git ignore rules per project plan |
+| **How** | Create file with patterns for: `.env`, `data/*/processed/`, `reports/`, `backups/`, `notebooks/*.html`, `__pycache__/`, `.venv/` |
+| **Inputs** | Project plan → Directory Rules |
+| **Outputs** | `.gitignore` |
+
+#### 1.1.3 Create `pyproject.toml`
+| Attribute | Value |
+|-----------|-------|
+| **What** | Python packaging config |
+| **How** | Define project metadata, dependencies, tool configs (ruff, mypy, pytest) |
+| **Inputs** | Tech stack versions from project plan |
+| **Outputs** | `pyproject.toml` |
+| **Dependencies** | PostgreSQL 16.x, Pydantic ≥2.0, SQLAlchemy ≥2.0, dbt-postgres ≥1.7, Pandas ≥2.1, GeoPandas ≥0.14, Dash ≥2.14, dash-mantine-components (latest), pytest ≥7.0 |
+
+#### 1.1.4 Create `.python-version`
+| Attribute | Value |
+|-----------|-------|
+| **What** | pyenv version file |
+| **How** | Single line: `3.11` or specific patch version |
+| **Outputs** | `.python-version` |
+
+#### 1.1.5 Create `.env.example`
+| Attribute | Value |
+|-----------|-------|
+| **What** | Environment variable template |
+| **How** | Template with: DATABASE_URL, POSTGRES_USER, POSTGRES_PASSWORD, POSTGRES_DB, DASH_DEBUG, SECRET_KEY, LOG_LEVEL |
+| **Inputs** | Project plan → Environment Setup |
+| **Outputs** | `.env.example` |
+
+#### 1.1.6 Create `README.md` (initial)
+| Attribute | Value |
+|-----------|-------|
+| **What** | Project overview stub |
+| **How** | Title, brief description, "Setup coming soon" |
+| **Outputs** | `README.md` |
+
+#### 1.1.7 Create `CLAUDE.md`
+| Attribute | Value |
+|-----------|-------|
+| **What** | AI assistant context file |
+| **How** | Project context, architecture decisions, patterns, conventions |
+| **Inputs** | Project plan → Code Architecture |
+| **Outputs** | `CLAUDE.md` |
+| **Why** | Claude Code effectiveness from day 1 |
+
+#### 1.1.8 Create `Makefile`
+| Attribute | Value |
+|-----------|-------|
+| **What** | All make targets from project plan |
+| **How** | Implement targets: setup, venv, clean, docker-up/down/logs/rebuild, db-init/backup/restore/reset, run, run-prod, dbt-run/test/docs/fresh, test, test-cov, lint, format, typecheck, ci, deploy, rollback |
+| **Inputs** | Project plan → Makefile Targets |
+| **Outputs** | `Makefile` |
+
+### 1.2 Infrastructure
+
+#### 1.2.4 Create `docker-compose.yml`
+| Attribute | Value |
+|-----------|-------|
+| **What** | Docker Compose V2 for PostgreSQL 16 + PostGIS |
+| **How** | Service definition, volume mounts, port 5432, env vars from `.env` |
+| **Inputs** | `.env.example` |
+| **Outputs** | `docker-compose.yml` |
+| **Note** | No `version` field (Docker Compose V2) |
+
+#### 1.2.5 Create `scripts/` directory structure
+| Attribute | Value |
+|-----------|-------|
+| **What** | Full scripts tree per project plan |
+| **How** | `mkdir -p scripts/{db,docker,deploy,dbt,dev}` |
+| **Outputs** | `scripts/db/`, `scripts/docker/`, `scripts/deploy/`, `scripts/dbt/`, `scripts/dev/` |
+
+#### 1.2.10 Create `scripts/db/init.sh`
+| Attribute | Value |
+|-----------|-------|
+| **What** | Database initialization with PostGIS |
+| **How** | `CREATE DATABASE`, `CREATE EXTENSION postgis`, schema creation |
+| **Standard** | `set -euo pipefail`, usage comment, idempotent |
+| **Outputs** | `scripts/db/init.sh` |
+
+### 1.3 Application Foundation
+
+#### 1.3.1 Create `portfolio_app/` directory structure
+| Attribute | Value |
+|-----------|-------|
+| **What** | Full application tree per project plan |
+| **Directories** | `portfolio_app/`, `portfolio_app/assets/`, `portfolio_app/assets/images/`, `portfolio_app/pages/`, `portfolio_app/pages/toronto/`, `portfolio_app/pages/toronto/callbacks/`, `portfolio_app/components/`, `portfolio_app/figures/`, `portfolio_app/toronto/`, `portfolio_app/toronto/parsers/`, `portfolio_app/toronto/loaders/`, `portfolio_app/toronto/schemas/`, `portfolio_app/toronto/models/`, `portfolio_app/toronto/transforms/`, `portfolio_app/errors/` |
+| **Pattern** | Callbacks in `pages/{dashboard}/callbacks/` per project plan |
+
+#### 1.3.3 Create `config.py`
+| Attribute | Value |
+|-----------|-------|
+| **What** | Pydantic BaseSettings for config |
+| **How** | Settings class loading from `.env` |
+| **Fields** | DATABASE_URL, POSTGRES_USER, POSTGRES_PASSWORD, POSTGRES_DB, DASH_DEBUG, SECRET_KEY, LOG_LEVEL |
+
+#### 1.3.5 Create `exceptions.py`
+| Attribute | Value |
+|-----------|-------|
+| **What** | Exception hierarchy per project plan |
+| **Classes** | `PortfolioError` (base), `ParseError`, `ValidationError`, `LoadError` |
+
+#### 1.3.6 Create `handlers.py`
+| Attribute | Value |
+|-----------|-------|
+| **What** | Error handling decorators |
+| **How** | Decorators for: logging/re-raise, retry logic, transaction boundaries, timing |
+| **Pattern** | Infrastructure concerns only; domain logic uses explicit handling |
+
+#### 1.3.7 Create `app.py`
+| Attribute | Value |
+|-----------|-------|
+| **What** | Dash app factory with Pages routing |
+| **How** | `Dash(__name__, use_pages=True)`, MantineProvider wrapper |
+| **Imports** | External: absolute; Internal: relative (dot notation) |
+
+#### 1.3.16 Create `conftest.py`
+| Attribute | Value |
+|-----------|-------|
+| **What** | pytest fixtures |
+| **How** | Test database fixture, sample data fixtures, app client fixture |
+
+### 1.4 Bio Page
+
+#### 1.4.7 Integrate bio content
+| Attribute | Value |
+|-----------|-------|
+| **What** | Content from `bio_content_v2.md` |
+| **Sections** | Headline, Professional Summary, Tech Stack, Side Project, Availability |
+| **Layout** | Hero → Summary → Tech Stack → Project Cards → Social Links → Availability |
+
+#### 1.4.8 Replace social link placeholders
+| Attribute | Value |
+|-----------|-------|
+| **What** | Replace `[USERNAME]` in LinkedIn/GitHub URLs |
+| **Source** | `bio_content_v2.md` → Social Links |
+| **Acceptance** | No placeholder text in production |
+
+#### 1.4.9 Implement project cards
+| Attribute | Value |
+|-----------|-------|
+| **What** | Dynamic project card display |
+| **Logic** | Show deployed projects with links; show "In Development" for in-progress; hide or grey out planned |
+| **Source** | `bio_content_v2.md` → Portfolio Projects Section |
+
+### 2.1 Data Acquisition
+
+#### 2.1.1 Define TRREB year scope + download PDFs
+| Attribute | Value |
+|-----------|-------|
+| **What** | Decide which years to parse for V1, download PDFs |
+| **Decision** | 2020—present for V1 (manageable scope, consistent PDF format). Expand to 2007+ in future if needed. |
+| **Output** | `data/toronto/raw/trreb/market_watch_YYYY_MM.pdf` |
+| **Note** | PDF format may vary pre-2018; test before committing to older years |
+
+#### 2.1.2 Digitize TRREB district boundaries
+| Attribute | Value |
+|-----------|-------|
+| **What** | GeoJSON with ~35 district polygons |
+| **Tool** | QGIS |
+| **Process** | Import PDF as raster → create vector layer → trace polygons → add attributes (district_code, district_name, area_type) → export GeoJSON (WGS84/EPSG:4326) |
+| **Input** | TRREB Toronto.pdf map |
+| **Output** | `data/toronto/raw/geo/trreb_districts.geojson` |
+| **Effort** | High |
+| **Complexity** | High |
+| **Note** | HUMAN TASK — not automatable |
+
+#### 2.1.5 Extract CMHC zone boundaries
+| Attribute | Value |
+|-----------|-------|
+| **What** | GeoJSON with ~20 zone polygons |
+| **Tool** | R with cmhc and sf packages |
+| **Process** | `get_cmhc_geography(geography_type="ZONE", cma="Toronto")` → `st_write()` to GeoJSON |
+| **Output** | `data/toronto/raw/geo/cmhc_zones.geojson` |
+
+#### 2.1.9 Research Tier 1 policy events
+| Attribute | Value |
+|-----------|-------|
+| **What** | Federal/provincial policy events with dates, descriptions, expected direction |
+| **Sources** | Bank of Canada, OSFI, Ontario Legislature |
+| **Schema** | event_date, effective_date, level, category, title, description, expected_direction, source_url, confidence |
+| **Acceptance** | Minimum 10 events, maximum 20 |
+| **Examples** | BoC rate decisions, OSFI B-20, Ontario Fair Housing Plan, foreign buyer tax |
+
+#### 2.1.13 Test TRREB parser across year boundaries
+| Attribute | Value |
+|-----------|-------|
+| **What** | Verify parser handles PDFs from different years |
+| **Test Cases** | 2020 Q1, 2022 Q1, 2024 Q1 (minimum) |
+| **Check For** | Table structure changes, column naming variations, page number shifts |
+| **Output** | Documented format variations, parser fallbacks if needed |
+
+### 2.2 Data Processing
+
+#### 2.2.3 Build TRREB PDF parser
+| Attribute | Value |
+|-----------|-------|
+| **What** | Extract summary tables from TRREB PDFs |
+| **Tool** | pdfplumber or camelot-py |
+| **Location** | Pages 3-4 (Summary by Area) |
+| **Fields** | report_date, area_code, area_name, area_type, sales, dollar_volume, avg_price, median_price, new_listings, active_listings, avg_sp_lp, avg_dom |
+| **Output** | `portfolio_app/toronto/parsers/trreb.py` |
+
+#### 2.2.7 CMHC reliability code handling
+| Attribute | Value |
+|-----------|-------|
+| **What** | Parse reliability codes, handle suppression |
+| **Codes** | a (excellent), b (good), c (fair), d (poor/caution), ** (suppressed → NULL) |
+| **Implementation** | Pydantic validators, enum type |
+
+### 2.3 Database Layer
+
+#### 2.3.8 Create dimension models
+| Attribute | Value |
+|-----------|-------|
+| **What** | SQLAlchemy 2.0 models for dimensions |
+| **Tables** | `dim_time`, `dim_trreb_district`, `dim_cmhc_zone`, `dim_neighbourhood`, `dim_policy_event` |
+| **Geometry** | PostGIS geometry columns for districts, zones, neighbourhoods |
+| **Note** | `dim_neighbourhood` has no FK to facts in V1 |
+
+#### 2.3.9 Create fact models
+| Attribute | Value |
+|-----------|-------|
+| **What** | SQLAlchemy 2.0 models for facts |
+| **Tables** | `fact_purchases`, `fact_rentals` |
+| **FKs** | fact_purchases → dim_time, dim_trreb_district; fact_rentals → dim_time, dim_cmhc_zone |
+
+### 2.4 dbt Transformation
+
+#### 2.4.8 Create staging models
+| Attribute | Value |
+|-----------|-------|
+| **What** | 1:1 source mapping, cleaned and typed |
+| **Models** | `stg_trreb__monthly`, `stg_cmhc__rental` |
+| **Naming** | `stg_{source}__{entity}` |
+
+#### 2.4.11 Create dbt schema tests
+| Attribute | Value |
+|-----------|-------|
+| **What** | Data quality tests |
+| **Tests** | `unique` (PKs), `not_null` (required), `accepted_values` (reliability codes, area_type), `relationships` (FK integrity) |
+
+#### 2.4.12 Create custom dbt tests
+| Attribute | Value |
+|-----------|-------|
+| **What** | Anomaly detection rules |
+| **Rules** | Price MoM change >30% → flag; missing districts → fail; duplicate records → fail |
+
+### 2.5 Visualization
+
+#### 2.5.2 Build choropleth factory
+| Attribute | Value |
+|-----------|-------|
+| **What** | Reusable choropleth_mapbox figure generator |
+| **Inputs** | GeoDataFrame, metric column, color config |
+| **Output** | Plotly figure |
+| **Location** | `portfolio_app/figures/choropleth.py` |
+
+#### 2.5.4—2.5.6 Statistical chart factories
+| Attribute | Value |
+|-----------|-------|
+| **What** | Statistical analysis visualizations |
+| **Charts** | YoY change with variance bands, seasonality decomposition, district correlation matrix |
+| **Location** | `portfolio_app/figures/statistical.py` |
+| **Why** | Required skill demonstration per project plan |
+
+#### 2.5.8 Create dashboard layout
+| Attribute | Value |
+|-----------|-------|
+| **What** | Toronto dashboard page structure |
+| **File** | `portfolio_app/pages/toronto/dashboard.py` |
+| **Pattern** | Layout only — no callbacks in this file |
+| **Components** | Navbar, choropleth map, time controls, layer toggles, time series panel, statistics panel, footer |
+
+#### 2.5.13—2.5.16 Create callbacks
+| Attribute | Value |
+|-----------|-------|
+| **What** | Dashboard interaction logic |
+| **Location** | `portfolio_app/pages/toronto/callbacks/` |
+| **Files** | `__init__.py`, `map_callbacks.py`, `filter_callbacks.py`, `timeseries_callbacks.py` |
+| **Pattern** | Separate from layout per project plan callback separation pattern |
+| **Registration** | Import callback modules in `callbacks/__init__.py`; import that package in `dashboard.py`. Dash Pages auto-discovers callbacks when module is imported. |
+
+#### 2.5.22 Test dashboard renders with sample data
+| Attribute | Value |
+|-----------|-------|
+| **What** | Verify dashboard works end-to-end |
+| **Sample Data** | Use output from task 2.3.12 (fact loaders). Run loaders with subset of parsed data before this task. |
+| **Verify** | Choropleth renders, time controls work, tooltips display, no console errors |
+
+---
+
+## Sprint Plan
+
+### Sprint 1: Project Bootstrap + Start TRREB Digitization
+
+**Goal**: Dev environment working, repo initialized, TRREB digitization started
+
+| Task ID | Task | Effort |
+|---------|------|--------|
+| 1.1.1 | Git repo init | Low |
+| 1.1.2 | .gitignore | Low |
+| 1.1.3 | pyproject.toml | Low |
+| 1.1.4 | .python-version | Low |
+| 1.1.5 | .env.example | Low |
+| 1.1.6 | README.md (initial) | Low |
+| 1.1.7 | CLAUDE.md | Low |
+| 1.1.8 | Makefile | Low |
+| 1.2.1 | Python env setup | Low |
+| 1.2.2 | .pre-commit-config.yaml | Low |
+| 1.2.3 | Install pre-commit | Low |
+| 1.2.4 | docker-compose.yml | Low |
+| 1.2.5 | scripts/ directory structure | Low |
+| 1.2.6—1.2.9 | Docker scripts | Low |
+| 1.2.10 | scripts/db/init.sh | Low |
+| 1.2.11 | scripts/dev/setup.sh | Low |
+| 1.2.12 | Verify Docker + PostGIS | Low |
+| 1.3.1 | portfolio_app/ directory structure | Low |
+| 1.3.2—1.3.6 | App foundation files | Low |
+| 1.3.14—1.3.17 | Test infrastructure | Low |
+| 2.1.1 | Download TRREB PDFs | Low |
+| 2.1.2 | **START** TRREB boundaries (HUMAN) | High |
+| 2.1.9 | **START** Policy events research | Mid |
+
+---
+
+### Sprint 2: Bio Page + Data Acquisition
+
+**Goal**: Bio live, all raw data downloaded
+
+| Task ID | Task | Effort |
+|---------|------|--------|
+| 1.3.7 | app.py with Pages | Low |
+| 1.3.8 | Theme config | Low |
+| 1.3.9—1.3.13 | Assets directory + files | Low |
+| 1.4.1—1.4.4 | Components | Low |
+| 1.4.5—1.4.10 | Bio page | Low |
+| 1.5.1—1.5.3 | VPS setup | Low |
+| 1.5.4—1.5.6 | Gunicorn/Nginx/SSL | Low |
+| 1.5.7—1.5.8 | Deploy scripts | Low |
+| 1.5.9—1.5.10 | Deploy + verify | Low |
+| 2.1.2 | **CONTINUE** TRREB boundaries | High |
+| 2.1.3—2.1.4 | CMHC registration + export | Low |
+| 2.1.5 | CMHC zone boundaries (R) | Low |
+| 2.1.6 | Neighbourhoods GeoJSON | Low |
+| 2.1.7 | Neighbourhood Profiles download | Low |
+| 2.1.9 | **CONTINUE** Policy events research | Mid |
+| 2.1.10 | policy_events.csv | Low |
+| 2.1.11—2.1.12 | data/ directory + organize | Low |
+
+**Milestone**: **Launch 1 — Bio Live**
+
+---
+
+### Sprint 3: Parsers + Schemas + Models
+
+**Goal**: ETL pipeline working, database layer complete
+
+| Task ID | Task | Effort |
+|---------|------|--------|
+| 2.1.2 | **COMPLETE** TRREB boundaries | High |
+| 2.1.8 | CRS validation | Low |
+| 2.2.1—2.2.2 | Toronto module init | Low |
+| 2.2.3—2.2.5 | TRREB parser + tests | Mid |
+| 2.2.6—2.2.8 | CMHC processor + tests | Low |
+| 2.2.9 | Neighbourhood Profiles parser | Low |
+| 2.2.10 | Policy events loader | Low |
+| 2.3.1—2.3.5 | Pydantic schemas | Low |
+| 2.3.6—2.3.9 | SQLAlchemy models | Low |
+
+---
+
+### Sprint 4: Loaders + dbt
+
+**Goal**: Data loaded, transformation layer ready
+
+| Task ID | Task | Effort |
+|---------|------|--------|
+| 2.3.10—2.3.13 | Loaders + tests | Mid |
+| 2.3.14 | SQL views | Low |
+| 2.4.1—2.4.7 | dbt setup + scripts | Low |
+| 2.4.8—2.4.10 | dbt models | Low |
+| 2.4.11—2.4.12 | dbt tests | Low |
+| 2.4.13 | dbt documentation | Low |
+| 2.7.1—2.7.3 | DB backup/restore scripts | Low |
+
+---
+
+### Sprint 5: Visualization
+
+**Goal**: Dashboard functional
+
+| Task ID | Task | Effort |
+|---------|------|--------|
+| 2.5.1—2.5.6 | Figure factories | Mid |
+| 2.5.7—2.5.12 | Dashboard layout + controls | Mid |
+| 2.5.13—2.5.16 | Callbacks | Mid |
+| 2.5.17—2.5.21 | Tooltips + overlays + markers | Low |
+| 2.5.22 | Test dashboard | Low |
+
+---
+
+### Sprint 6: Polish + Launch 2
+
+**Goal**: Dashboard deployed
+
+| Task ID | Task | Effort |
+|---------|------|--------|
+| 2.6.1—2.6.6 | Documentation | Low |
+| 2.7.4—2.7.5 | Rollback script + retention | Low |
+| 2.7.6—2.7.7 | Health endpoint + monitoring | Low |
+| 2.7.8—2.7.9 | Deploy + verify | Low |
+
+**Milestone**: **Launch 2 — Toronto Dashboard Live**
+
+---
+
+### Sprint 7: Buffer
+
+**Goal**: Contingency for slippage, bug fixes
+
+| Task ID | Task | Effort |
+|---------|------|--------|
+| — | Overflow from previous sprints | Varies |
+| — | Bug fixes | Varies |
+| — | UX polish | Low |
+
+---
+
+## Sprint Summary
+
+| Sprint | Focus | Key Risk | Milestone |
+|--------|-------|----------|-----------|
+| 1 | Bootstrap + start boundaries | — | — |
+| 2 | Bio + data acquisition | TRREB digitization | Launch 1 |
+| 3 | Parsers + DB layer | PDF parser, boundaries | — |
+| 4 | Loaders + dbt | — | — |
+| 5 | Visualization | Choropleth complexity | — |
+| 6 | Polish + deploy | — | Launch 2 |
+| 7 | Buffer | — | — |
+
+---
+
+## Dependency Graph
+
+### Launch 1 Critical Path
+```
+1.1.1 → 1.1.3 → 1.2.1 → 1.3.1 → 1.3.7 → 1.4.6 → 1.4.10 → 1.5.9 → 1.5.10
+```
+
+### Launch 2 Critical Path
+```
+2.1.2 (TRREB boundaries) ─┬→ 2.1.8 (CRS) → 2.5.2 (choropleth) → 2.5.8 (layout) → 2.5.22 (test) → 2.7.8 (deploy)
+                          │
+2.1.1 → 2.2.3 (parser) → 2.2.4 → 2.3.12 (loaders) → 2.4.8 (dbt) ─┘
+```
+
+### Parallel Tracks (can run simultaneously)
+
+| Track | Tasks | Can Start |
+|-------|-------|-----------|
+| **A: TRREB Boundaries** | 2.1.1 → 2.1.2 | Sprint 1 |
+| **B: TRREB Parser** | 2.2.3—2.2.5 | Sprint 2 (after PDFs) |
+| **C: CMHC** | 2.1.3—2.1.5 → 2.2.6—2.2.8 | Sprint 2 |
+| **D: Enrichment** | 2.1.6—2.1.7 → 2.2.9 | Sprint 2 |
+| **E: Policy Events** | 2.1.9—2.1.10 → 2.2.10 | Sprint 1—2 |
+| **F: Schemas/Models** | 2.3.1—2.3.9 | Sprint 3 (after parsers) |
+| **G: dbt** | 2.4.* | Sprint 4 (after loaders) |
+| **H: Ops Scripts** | 2.7.1—2.7.5 | Sprint 4 |
+
+---
+
+## Risk Register
+
+| Risk | Likelihood | Impact | Mitigation |
+|------|------------|--------|------------|
+| TRREB digitization slips | Medium | High | Start Sprint 1; timebox; accept lower precision initially |
+| PDF parser breaks on older years | Medium | Medium | Test multiple years early; build fallbacks |
+| PostGIS geometry issues | Low | Medium | Validate CRS before load (2.1.8) |
+| Choropleth performance | Low | Medium | Pre-aggregate; simplify geometries |
+| Policy events research takes too long | Medium | Low | Cap at 10 events minimum; expand post-launch |
+
+---
+
+## Acceptance Criteria
+
+### Launch 1
+- [ ] Bio page accessible via HTTPS
+- [ ] All content from `bio_content_v2.md` rendered
+- [ ] No placeholder text ([USERNAME]) visible
+- [ ] Mobile responsive
+- [ ] Social links functional
+
+### Launch 2
+- [ ] Choropleth renders TRREB districts
+- [ ] Choropleth renders CMHC zones
+- [ ] Purchase/rental mode toggle works
+- [ ] Time navigation works (monthly for TRREB, annual for CMHC)
+- [ ] Policy event markers visible on time series
+- [ ] Neighbourhood overlay toggleable
+- [ ] Methodology documentation published
+- [ ] Data sources cited
+- [ ] Health endpoint responds
+
+---
+
+## Effort Legend
+
+| Level | Meaning |
+|-------|---------|
+| **Low** | Straightforward; minimal iteration expected |
+| **Mid** | Requires debugging or multi-step coordination |
+| **High** | Complex logic, external tools, or human intervention required |
+
+---
+
+*Document Version: 4.1*
+*Created: January 2026*

portfolio_app/__init__.py

@@ -0,0 +1,3 @@
+"""Analytics Portfolio Application."""
+
+__version__ = "0.1.0"

portfolio_app/app.py

@@ -0,0 +1,58 @@
+"""Dash application factory with Pages routing."""
+
+import dash
+import dash_mantine_components as dmc
+from dash import dcc, html
+
+from .components import create_sidebar
+from .config import get_settings
+
+
+def create_app() -> dash.Dash:
+    """Create and configure the Dash application."""
+    app = dash.Dash(
+        __name__,
+        use_pages=True,
+        suppress_callback_exceptions=True,
+        title="Analytics Portfolio",
+        external_stylesheets=dmc.styles.ALL,
+    )
+
+    app.layout = dmc.MantineProvider(
+        id="mantine-provider",
+        children=[
+            dcc.Location(id="url", refresh=False),
+            dcc.Store(id="theme-store", storage_type="local", data="dark"),
+            dcc.Store(id="theme-init-dummy"),  # Dummy store for theme init callback
+            html.Div(
+                [
+                    create_sidebar(),
+                    html.Div(
+                        dash.page_container,
+                        className="page-content-wrapper",
+                    ),
+                ],
+            ),
+        ],
+        theme={
+            "primaryColor": "blue",
+            "fontFamily": "'Inter', sans-serif",
+        },
+        defaultColorScheme="dark",
+    )
+
+    # Import callbacks to register them
+    from . import callbacks  # noqa: F401
+
+    return app
+
+
+def main() -> None:
+    """Run the development server."""
+    settings = get_settings()
+    app = create_app()
+    app.run(debug=settings.dash_debug, host="0.0.0.0", port=8050)
+
+
+if __name__ == "__main__":
+    main()

portfolio_app/assets/sidebar.css

@@ -0,0 +1,139 @@
+/* Floating sidebar navigation styles */
+
+/* Sidebar container */
+.floating-sidebar {
+    position: fixed;
+    left: 16px;
+    top: 50%;
+    transform: translateY(-50%);
+    width: 60px;
+    padding: 16px 8px;
+    border-radius: 32px;
+    z-index: 1000;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    gap: 8px;
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15);
+    transition: background-color 0.2s ease;
+}
+
+/* Page content offset to prevent sidebar overlap */
+.page-content-wrapper {
+    margin-left: 92px; /* sidebar width (60px) + left margin (16px) + gap (16px) */
+    min-height: 100vh;
+}
+
+/* Dark theme (default) */
+[data-mantine-color-scheme="dark"] .floating-sidebar {
+    background-color: #141414;
+}
+
+[data-mantine-color-scheme="dark"] body {
+    background-color: #000000;
+}
+
+/* Light theme */
+[data-mantine-color-scheme="light"] .floating-sidebar {
+    background-color: #f0f0f0;
+}
+
+[data-mantine-color-scheme="light"] body {
+    background-color: #ffffff;
+}
+
+/* Brand initials styling */
+.sidebar-brand {
+    width: 40px;
+    height: 40px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    border-radius: 50%;
+    background-color: var(--mantine-color-blue-filled);
+    margin-bottom: 4px;
+    transition: transform 0.2s ease;
+}
+
+.sidebar-brand:hover {
+    transform: scale(1.05);
+}
+
+.sidebar-brand-link {
+    font-weight: 700;
+    font-size: 16px;
+    color: white;
+    text-decoration: none;
+    line-height: 1;
+}
+
+/* Divider between sections */
+.sidebar-divider {
+    width: 32px;
+    height: 1px;
+    background-color: var(--mantine-color-dimmed);
+    margin: 4px 0;
+    opacity: 0.3;
+}
+
+/* Active nav icon indicator */
+.nav-icon-active {
+    background-color: var(--mantine-color-blue-filled) !important;
+}
+
+/* Navigation icon hover effects */
+.floating-sidebar .mantine-ActionIcon-root {
+    transition: transform 0.15s ease, background-color 0.15s ease;
+}
+
+.floating-sidebar .mantine-ActionIcon-root:hover {
+    transform: scale(1.1);
+}
+
+/* Ensure links don't have underlines */
+.floating-sidebar a {
+    text-decoration: none;
+}
+
+/* Theme toggle specific styling */
+#theme-toggle {
+    transition: transform 0.3s ease;
+}
+
+#theme-toggle:hover {
+    transform: rotate(15deg) scale(1.1);
+}
+
+/* Responsive adjustments for smaller screens */
+@media (max-width: 768px) {
+    .floating-sidebar {
+        left: 8px;
+        width: 50px;
+        padding: 12px 6px;
+        border-radius: 25px;
+    }
+
+    .page-content-wrapper {
+        margin-left: 70px;
+    }
+
+    .sidebar-brand {
+        width: 34px;
+        height: 34px;
+    }
+
+    .sidebar-brand-link {
+        font-size: 14px;
+    }
+}
+
+/* Very small screens - hide sidebar, show minimal navigation */
+@media (max-width: 480px) {
+    .floating-sidebar {
+        display: none;
+    }
+
+    .page-content-wrapper {
+        margin-left: 0;
+    }
+}

portfolio_app/callbacks/__init__.py

@@ -0,0 +1,5 @@
+"""Application-level callbacks for the portfolio app."""
+
+from . import theme
+
+__all__ = ["theme"]

portfolio_app/callbacks/theme.py

@@ -0,0 +1,38 @@
+"""Theme toggle callbacks using clientside JavaScript."""
+
+from dash import Input, Output, State, clientside_callback
+
+# Toggle theme on button click
+# Stores new theme value and updates the DOM attribute
+clientside_callback(
+    """
+    function(n_clicks, currentTheme) {
+        if (n_clicks === undefined || n_clicks === null) {
+            return window.dash_clientside.no_update;
+        }
+        const newTheme = currentTheme === 'dark' ? 'light' : 'dark';
+        document.documentElement.setAttribute('data-mantine-color-scheme', newTheme);
+        return newTheme;
+    }
+    """,
+    Output("theme-store", "data"),
+    Input("theme-toggle", "n_clicks"),
+    State("theme-store", "data"),
+    prevent_initial_call=True,
+)
+
+# Initialize theme from localStorage on page load
+# Uses a dummy output since we only need the side effect of setting the DOM attribute
+clientside_callback(
+    """
+    function(theme) {
+        if (theme) {
+            document.documentElement.setAttribute('data-mantine-color-scheme', theme);
+        }
+        return theme;
+    }
+    """,
+    Output("theme-init-dummy", "data"),
+    Input("theme-store", "data"),
+    prevent_initial_call=False,
+)

portfolio_app/components/__init__.py

@@ -0,0 +1,16 @@
+"""Shared Dash components for the portfolio application."""
+
+from .map_controls import create_map_controls, create_metric_selector
+from .metric_card import MetricCard, create_metric_cards_row
+from .sidebar import create_sidebar
+from .time_slider import create_time_slider, create_year_selector
+
+__all__ = [
+    "create_map_controls",
+    "create_metric_selector",
+    "create_sidebar",
+    "create_time_slider",
+    "create_year_selector",
+    "MetricCard",
+    "create_metric_cards_row",
+]

portfolio_app/components/map_controls.py

@@ -0,0 +1,79 @@
+"""Map control components for choropleth visualizations."""
+
+from typing import Any
+
+import dash_mantine_components as dmc
+from dash import html
+
+
+def create_metric_selector(
+    id_prefix: str,
+    options: list[dict[str, str]],
+    default_value: str | None = None,
+    label: str = "Select Metric",
+) -> dmc.Select:
+    """Create a metric selector dropdown.
+
+    Args:
+        id_prefix: Prefix for component IDs.
+        options: List of options with 'label' and 'value' keys.
+        default_value: Initial selected value.
+        label: Label text for the selector.
+
+    Returns:
+        Mantine Select component.
+    """
+    return dmc.Select(
+        id=f"{id_prefix}-metric-selector",
+        label=label,
+        data=options,
+        value=default_value or (options[0]["value"] if options else None),
+        style={"width": "200px"},
+    )
+
+
+def create_map_controls(
+    id_prefix: str,
+    metric_options: list[dict[str, str]],
+    default_metric: str | None = None,
+    show_layer_toggle: bool = True,
+) -> dmc.Paper:
+    """Create a control panel for map visualizations.
+
+    Args:
+        id_prefix: Prefix for component IDs.
+        metric_options: Options for metric selector.
+        default_metric: Default selected metric.
+        show_layer_toggle: Whether to show layer visibility toggle.
+
+    Returns:
+        Mantine Paper component containing controls.
+    """
+    controls: list[Any] = [
+        create_metric_selector(
+            id_prefix=id_prefix,
+            options=metric_options,
+            default_value=default_metric,
+            label="Display Metric",
+        ),
+    ]
+
+    if show_layer_toggle:
+        controls.append(
+            dmc.Switch(
+                id=f"{id_prefix}-layer-toggle",
+                label="Show Boundaries",
+                checked=True,
+                style={"marginTop": "10px"},
+            )
+        )
+
+    return dmc.Paper(
+        children=[
+            dmc.Text("Map Controls", fw=500, size="sm", mb="xs"),
+            html.Div(controls),
+        ],
+        p="md",
+        radius="sm",
+        withBorder=True,
+    )

portfolio_app/components/metric_card.py

@@ -0,0 +1,115 @@
+"""Metric card components for KPI display."""
+
+from typing import Any
+
+import dash_mantine_components as dmc
+from dash import dcc
+
+from portfolio_app.figures.summary_cards import create_metric_card_figure
+
+
+class MetricCard:
+    """A reusable metric card component."""
+
+    def __init__(
+        self,
+        id_prefix: str,
+        title: str,
+        value: float | int | str = 0,
+        delta: float | None = None,
+        prefix: str = "",
+        suffix: str = "",
+        format_spec: str = ",.0f",
+        positive_is_good: bool = True,
+    ):
+        """Initialize a metric card.
+
+        Args:
+            id_prefix: Prefix for component IDs.
+            title: Card title.
+            value: Main metric value.
+            delta: Change value for delta indicator.
+            prefix: Value prefix (e.g., '$').
+            suffix: Value suffix.
+            format_spec: Python format specification.
+            positive_is_good: Whether positive delta is good.
+        """
+        self.id_prefix = id_prefix
+        self.title = title
+        self.value = value
+        self.delta = delta
+        self.prefix = prefix
+        self.suffix = suffix
+        self.format_spec = format_spec
+        self.positive_is_good = positive_is_good
+
+    def render(self) -> dmc.Paper:
+        """Render the metric card component.
+
+        Returns:
+            Mantine Paper component with embedded graph.
+        """
+        fig = create_metric_card_figure(
+            value=self.value,
+            title=self.title,
+            delta=self.delta,
+            prefix=self.prefix,
+            suffix=self.suffix,
+            format_spec=self.format_spec,
+            positive_is_good=self.positive_is_good,
+        )
+
+        return dmc.Paper(
+            children=[
+                dcc.Graph(
+                    id=f"{self.id_prefix}-graph",
+                    figure=fig,
+                    config={"displayModeBar": False},
+                    style={"height": "120px"},
+                )
+            ],
+            p="xs",
+            radius="sm",
+            withBorder=True,
+        )
+
+
+def create_metric_cards_row(
+    metrics: list[dict[str, Any]],
+    id_prefix: str = "metric",
+) -> dmc.SimpleGrid:
+    """Create a row of metric cards.
+
+    Args:
+        metrics: List of metric configurations with keys:
+            - title: Card title
+            - value: Metric value
+            - delta: Optional change value
+            - prefix: Optional value prefix
+            - suffix: Optional value suffix
+            - format_spec: Optional format specification
+            - positive_is_good: Optional delta color logic
+        id_prefix: Prefix for component IDs.
+
+    Returns:
+        Mantine SimpleGrid component with metric cards.
+    """
+    cards = []
+    for i, metric in enumerate(metrics):
+        card = MetricCard(
+            id_prefix=f"{id_prefix}-{i}",
+            title=metric.get("title", ""),
+            value=metric.get("value", 0),
+            delta=metric.get("delta"),
+            prefix=metric.get("prefix", ""),
+            suffix=metric.get("suffix", ""),
+            format_spec=metric.get("format_spec", ",.0f"),
+            positive_is_good=metric.get("positive_is_good", True),
+        )
+        cards.append(card.render())
+
+    return dmc.SimpleGrid(
+        cols={"base": 1, "sm": 2, "md": len(cards)},
+        spacing="md",
+        children=cards,
+    )

portfolio_app/components/sidebar.py

@@ -0,0 +1,179 @@
+"""Floating sidebar navigation component."""
+
+import dash_mantine_components as dmc
+from dash import dcc, html
+from dash_iconify import DashIconify
+
+# Navigation items configuration
+NAV_ITEMS = [
+    {"path": "/", "icon": "tabler:home", "label": "Home"},
+    {"path": "/toronto", "icon": "tabler:map-2", "label": "Toronto Housing"},
+]
+
+# External links configuration
+EXTERNAL_LINKS = [
+    {
+        "url": "https://github.com/leomiranda",
+        "icon": "tabler:brand-github",
+        "label": "GitHub",
+    },
+    {
+        "url": "https://linkedin.com/in/leobmiranda",
+        "icon": "tabler:brand-linkedin",
+        "label": "LinkedIn",
+    },
+]
+
+
+def create_brand_logo() -> html.Div:
+    """Create the brand initials logo."""
+    return html.Div(
+        dcc.Link(
+            "LM",
+            href="/",
+            className="sidebar-brand-link",
+        ),
+        className="sidebar-brand",
+    )
+
+
+def create_nav_icon(
+    icon: str,
+    label: str,
+    path: str,
+    current_path: str,
+) -> dmc.Tooltip:
+    """Create a navigation icon with tooltip.
+
+    Args:
+        icon: Iconify icon string.
+        label: Tooltip label.
+        path: Navigation path.
+        current_path: Current page path for active state.
+
+    Returns:
+        Tooltip-wrapped navigation icon.
+    """
+    is_active = current_path == path or (path != "/" and current_path.startswith(path))
+
+    return dmc.Tooltip(
+        dcc.Link(
+            dmc.ActionIcon(
+                DashIconify(icon=icon, width=20),
+                variant="subtle" if not is_active else "filled",
+                size="lg",
+                radius="xl",
+                color="blue" if is_active else "gray",
+                className="nav-icon-active" if is_active else "",
+            ),
+            href=path,
+        ),
+        label=label,
+        position="right",
+        withArrow=True,
+    )
+
+
+def create_theme_toggle(current_theme: str = "dark") -> dmc.Tooltip:
+    """Create the theme toggle button.
+
+    Args:
+        current_theme: Current theme ('dark' or 'light').
+
+    Returns:
+        Tooltip-wrapped theme toggle icon.
+    """
+    icon = "tabler:sun" if current_theme == "dark" else "tabler:moon"
+    label = "Switch to light mode" if current_theme == "dark" else "Switch to dark mode"
+
+    return dmc.Tooltip(
+        dmc.ActionIcon(
+            DashIconify(icon=icon, width=20, id="theme-toggle-icon"),
+            id="theme-toggle",
+            variant="subtle",
+            size="lg",
+            radius="xl",
+            color="gray",
+        ),
+        label=label,
+        position="right",
+        withArrow=True,
+    )
+
+
+def create_external_link(url: str, icon: str, label: str) -> dmc.Tooltip:
+    """Create an external link icon with tooltip.
+
+    Args:
+        url: External URL.
+        icon: Iconify icon string.
+        label: Tooltip label.
+
+    Returns:
+        Tooltip-wrapped external link icon.
+    """
+    return dmc.Tooltip(
+        dmc.Anchor(
+            dmc.ActionIcon(
+                DashIconify(icon=icon, width=20),
+                variant="subtle",
+                size="lg",
+                radius="xl",
+                color="gray",
+            ),
+            href=url,
+            target="_blank",
+        ),
+        label=label,
+        position="right",
+        withArrow=True,
+    )
+
+
+def create_sidebar_divider() -> html.Div:
+    """Create a horizontal divider for the sidebar."""
+    return html.Div(className="sidebar-divider")
+
+
+def create_sidebar(current_path: str = "/", current_theme: str = "dark") -> html.Div:
+    """Create the floating sidebar navigation.
+
+    Args:
+        current_path: Current page path for active state highlighting.
+        current_theme: Current theme for toggle icon state.
+
+    Returns:
+        Complete sidebar component.
+    """
+    return html.Div(
+        [
+            # Brand logo
+            create_brand_logo(),
+            create_sidebar_divider(),
+            # Navigation icons
+            *[
+                create_nav_icon(
+                    icon=item["icon"],
+                    label=item["label"],
+                    path=item["path"],
+                    current_path=current_path,
+                )
+                for item in NAV_ITEMS
+            ],
+            create_sidebar_divider(),
+            # Theme toggle
+            create_theme_toggle(current_theme),
+            create_sidebar_divider(),
+            # External links
+            *[
+                create_external_link(
+                    url=link["url"],
+                    icon=link["icon"],
+                    label=link["label"],
+                )
+                for link in EXTERNAL_LINKS
+            ],
+        ],
+        className="floating-sidebar",
+        id="floating-sidebar",
+    )

portfolio_app/components/time_slider.py

@@ -0,0 +1,135 @@
+"""Time selection components for temporal data filtering."""
+
+from datetime import date
+
+import dash_mantine_components as dmc
+
+
+def create_year_selector(
+    id_prefix: str,
+    min_year: int = 2020,
+    max_year: int | None = None,
+    default_year: int | None = None,
+    label: str = "Select Year",
+) -> dmc.Select:
+    """Create a year selector dropdown.
+
+    Args:
+        id_prefix: Prefix for component IDs.
+        min_year: Minimum year option.
+        max_year: Maximum year option (defaults to current year).
+        default_year: Initial selected year.
+        label: Label text for the selector.
+
+    Returns:
+        Mantine Select component.
+    """
+    if max_year is None:
+        max_year = date.today().year
+
+    if default_year is None:
+        default_year = max_year
+
+    years = list(range(max_year, min_year - 1, -1))
+    options = [{"label": str(year), "value": str(year)} for year in years]
+
+    return dmc.Select(
+        id=f"{id_prefix}-year-selector",
+        label=label,
+        data=options,
+        value=str(default_year),
+        style={"width": "120px"},
+    )
+
+
+def create_time_slider(
+    id_prefix: str,
+    min_year: int = 2020,
+    max_year: int | None = None,
+    default_range: tuple[int, int] | None = None,
+    label: str = "Time Range",
+) -> dmc.Paper:
+    """Create a time range slider component.
+
+    Args:
+        id_prefix: Prefix for component IDs.
+        min_year: Minimum year for the slider.
+        max_year: Maximum year for the slider.
+        default_range: Default (start, end) year range.
+        label: Label text for the slider.
+
+    Returns:
+        Mantine Paper component containing the slider.
+    """
+    if max_year is None:
+        max_year = date.today().year
+
+    if default_range is None:
+        default_range = (min_year, max_year)
+
+    # Create marks for every year
+    marks = [
+        {"value": year, "label": str(year)} for year in range(min_year, max_year + 1)
+    ]
+
+    return dmc.Paper(
+        children=[
+            dmc.Text(label, fw=500, size="sm", mb="xs"),
+            dmc.RangeSlider(
+                id=f"{id_prefix}-time-slider",
+                min=min_year,
+                max=max_year,
+                value=list(default_range),
+                marks=marks,
+                step=1,
+                minRange=1,
+                style={"marginTop": "20px", "marginBottom": "10px"},
+            ),
+        ],
+        p="md",
+        radius="sm",
+        withBorder=True,
+    )
+
+
+def create_month_selector(
+    id_prefix: str,
+    default_month: int | None = None,
+    label: str = "Select Month",
+) -> dmc.Select:
+    """Create a month selector dropdown.
+
+    Args:
+        id_prefix: Prefix for component IDs.
+        default_month: Initial selected month (1-12).
+        label: Label text for the selector.
+
+    Returns:
+        Mantine Select component.
+    """
+    months = [
+        "January",
+        "February",
+        "March",
+        "April",
+        "May",
+        "June",
+        "July",
+        "August",
+        "September",
+        "October",
+        "November",
+        "December",
+    ]
+    options = [{"label": month, "value": str(i + 1)} for i, month in enumerate(months)]
+
+    if default_month is None:
+        default_month = date.today().month
+
+    return dmc.Select(
+        id=f"{id_prefix}-month-selector",
+        label=label,
+        data=options,
+        value=str(default_month),
+        style={"width": "140px"},
+    )

portfolio_app/config.py

@@ -0,0 +1,34 @@
+"""Application configuration using Pydantic BaseSettings."""
+
+from functools import lru_cache
+
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class Settings(BaseSettings):  # type: ignore[misc]
+    """Application settings loaded from environment variables."""
+
+    model_config = SettingsConfigDict(
+        env_file=".env",
+        env_file_encoding="utf-8",
+        extra="ignore",
+    )
+
+    # Database
+    database_url: str = "postgresql://portfolio:portfolio_dev@localhost:5432/portfolio"
+    postgres_user: str = "portfolio"
+    postgres_password: str = "portfolio_dev"
+    postgres_db: str = "portfolio"
+
+    # Application
+    dash_debug: bool = True
+    secret_key: str = "change-me-in-production"
+
+    # Logging
+    log_level: str = "INFO"
+
+
+@lru_cache
+def get_settings() -> Settings:
+    """Get cached settings instance."""
+    return Settings()

portfolio_app/errors/__init__.py

@@ -0,0 +1,5 @@
+"""Error handling for the portfolio application."""
+
+from .exceptions import LoadError, ParseError, PortfolioError, ValidationError
+
+__all__ = ["PortfolioError", "ParseError", "ValidationError", "LoadError"]

portfolio_app/errors/exceptions.py

@@ -0,0 +1,17 @@
+"""Custom exceptions for the portfolio application."""
+
+
+class PortfolioError(Exception):
+    """Base exception for all portfolio errors."""
+
+
+class ParseError(PortfolioError):
+    """PDF/CSV parsing failed."""
+
+
+class ValidationError(PortfolioError):
+    """Pydantic or business rule validation failed."""
+
+
+class LoadError(PortfolioError):
+    """Database load operation failed."""

portfolio_app/figures/__init__.py

@@ -0,0 +1,31 @@
+"""Plotly figure factories for data visualization."""
+
+from .choropleth import (
+    create_choropleth_figure,
+    create_district_map,
+    create_zone_map,
+)
+from .summary_cards import create_metric_card_figure, create_summary_metrics
+from .time_series import (
+    add_policy_markers,
+    create_market_comparison_chart,
+    create_price_time_series,
+    create_time_series_with_events,
+    create_volume_time_series,
+)
+
+__all__ = [
+    # Choropleth
+    "create_choropleth_figure",
+    "create_district_map",
+    "create_zone_map",
+    # Time series
+    "create_price_time_series",
+    "create_volume_time_series",
+    "create_market_comparison_chart",
+    "create_time_series_with_events",
+    "add_policy_markers",
+    # Summary
+    "create_metric_card_figure",
+    "create_summary_metrics",
+]

portfolio_app/figures/choropleth.py

@@ -0,0 +1,171 @@
+"""Choropleth map figure factory for Toronto housing data."""
+
+from typing import Any
+
+import plotly.express as px
+import plotly.graph_objects as go
+
+
+def create_choropleth_figure(
+    geojson: dict[str, Any] | None,
+    data: list[dict[str, Any]],
+    location_key: str,
+    color_column: str,
+    hover_data: list[str] | None = None,
+    color_scale: str = "Blues",
+    title: str | None = None,
+    map_style: str = "carto-positron",
+    center: dict[str, float] | None = None,
+    zoom: float = 9.5,
+) -> go.Figure:
+    """Create a choropleth map figure.
+
+    Args:
+        geojson: GeoJSON FeatureCollection for boundaries.
+        data: List of data records with location keys and values.
+        location_key: Column name for location identifier.
+        color_column: Column name for color values.
+        hover_data: Additional columns to show on hover.
+        color_scale: Plotly color scale name.
+        title: Optional chart title.
+        map_style: Mapbox style (carto-positron, open-street-map, etc.).
+        center: Map center coordinates {"lat": float, "lon": float}.
+        zoom: Initial zoom level.
+
+    Returns:
+        Plotly Figure object.
+    """
+    # Default center to Toronto
+    if center is None:
+        center = {"lat": 43.7, "lon": -79.4}
+
+    # Use dark-mode friendly map style by default
+    if map_style == "carto-positron":
+        map_style = "carto-darkmatter"
+
+    # If no geojson provided, create a placeholder map
+    if geojson is None or not data:
+        fig = go.Figure(go.Scattermapbox())
+        fig.update_layout(
+            mapbox={
+                "style": map_style,
+                "center": center,
+                "zoom": zoom,
+            },
+            margin={"l": 0, "r": 0, "t": 40, "b": 0},
+            title=title or "Toronto Housing Map",
+            height=500,
+            paper_bgcolor="rgba(0,0,0,0)",
+            plot_bgcolor="rgba(0,0,0,0)",
+            font_color="#c9c9c9",
+        )
+        fig.add_annotation(
+            text="No geometry data available. Complete QGIS digitization to enable map.",
+            xref="paper",
+            yref="paper",
+            x=0.5,
+            y=0.5,
+            showarrow=False,
+            font={"size": 14, "color": "#888888"},
+        )
+        return fig
+
+    # Create choropleth with data
+    import pandas as pd
+
+    df = pd.DataFrame(data)
+
+    # Use dark-mode friendly map style
+    effective_map_style = (
+        "carto-darkmatter" if map_style == "carto-positron" else map_style
+    )
+
+    fig = px.choropleth_mapbox(
+        df,
+        geojson=geojson,
+        locations=location_key,
+        featureidkey=f"properties.{location_key}",
+        color=color_column,
+        color_continuous_scale=color_scale,
+        hover_data=hover_data,
+        mapbox_style=effective_map_style,
+        center=center,
+        zoom=zoom,
+        opacity=0.7,
+    )
+
+    fig.update_layout(
+        margin={"l": 0, "r": 0, "t": 40, "b": 0},
+        title=title,
+        height=500,
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        coloraxis_colorbar={
+            "title": {
+                "text": color_column.replace("_", " ").title(),
+                "font": {"color": "#c9c9c9"},
+            },
+            "thickness": 15,
+            "len": 0.7,
+            "tickfont": {"color": "#c9c9c9"},
+        },
+    )
+
+    return fig
+
+
+def create_district_map(
+    districts_geojson: dict[str, Any] | None,
+    purchase_data: list[dict[str, Any]],
+    metric: str = "avg_price",
+) -> go.Figure:
+    """Create choropleth map for TRREB districts.
+
+    Args:
+        districts_geojson: GeoJSON for TRREB district boundaries.
+        purchase_data: Purchase statistics by district.
+        metric: Metric to display (avg_price, sales_count, etc.).
+
+    Returns:
+        Plotly Figure object.
+    """
+    hover_columns = ["district_name", "sales_count", "avg_price", "median_price"]
+
+    return create_choropleth_figure(
+        geojson=districts_geojson,
+        data=purchase_data,
+        location_key="district_code",
+        color_column=metric,
+        hover_data=[c for c in hover_columns if c != metric],
+        color_scale="Blues" if "price" in metric else "Greens",
+        title="Toronto Purchase Market by District",
+    )
+
+
+def create_zone_map(
+    zones_geojson: dict[str, Any] | None,
+    rental_data: list[dict[str, Any]],
+    metric: str = "avg_rent",
+) -> go.Figure:
+    """Create choropleth map for CMHC zones.
+
+    Args:
+        zones_geojson: GeoJSON for CMHC zone boundaries.
+        rental_data: Rental statistics by zone.
+        metric: Metric to display (avg_rent, vacancy_rate, etc.).
+
+    Returns:
+        Plotly Figure object.
+    """
+    hover_columns = ["zone_name", "avg_rent", "vacancy_rate", "rental_universe"]
+
+    return create_choropleth_figure(
+        geojson=zones_geojson,
+        data=rental_data,
+        location_key="zone_code",
+        color_column=metric,
+        hover_data=[c for c in hover_columns if c != metric],
+        color_scale="Oranges" if "rent" in metric else "Purples",
+        title="Toronto Rental Market by Zone",
+    )

portfolio_app/figures/summary_cards.py

@@ -0,0 +1,107 @@
+"""Summary card figure factories for KPI display."""
+
+from typing import Any
+
+import plotly.graph_objects as go
+
+
+def create_metric_card_figure(
+    value: float | int | str,
+    title: str,
+    delta: float | None = None,
+    delta_suffix: str = "%",
+    prefix: str = "",
+    suffix: str = "",
+    format_spec: str = ",.0f",
+    positive_is_good: bool = True,
+) -> go.Figure:
+    """Create a KPI indicator figure.
+
+    Args:
+        value: The main metric value.
+        title: Card title.
+        delta: Optional change value (for delta indicator).
+        delta_suffix: Suffix for delta value (e.g., '%').
+        prefix: Prefix for main value (e.g., '$').
+        suffix: Suffix for main value.
+        format_spec: Python format specification for the value.
+        positive_is_good: Whether positive delta is good (green) or bad (red).
+
+    Returns:
+        Plotly Figure object.
+    """
+    # Determine numeric value for indicator
+    if isinstance(value, int | float):
+        number_value: float | None = float(value)
+    else:
+        number_value = None
+
+    fig = go.Figure()
+
+    # Add indicator trace
+    indicator_config: dict[str, Any] = {
+        "mode": "number",
+        "value": number_value if number_value is not None else 0,
+        "title": {"text": title, "font": {"size": 14}},
+        "number": {
+            "font": {"size": 32},
+            "prefix": prefix,
+            "suffix": suffix,
+            "valueformat": format_spec,
+        },
+    }
+
+    # Add delta if provided
+    if delta is not None:
+        indicator_config["mode"] = "number+delta"
+        indicator_config["delta"] = {
+            "reference": number_value - delta if number_value else 0,
+            "relative": False,
+            "valueformat": ".1f",
+            "suffix": delta_suffix,
+            "increasing": {"color": "green" if positive_is_good else "red"},
+            "decreasing": {"color": "red" if positive_is_good else "green"},
+        }
+
+    fig.add_trace(go.Indicator(**indicator_config))
+
+    fig.update_layout(
+        height=120,
+        margin={"l": 20, "r": 20, "t": 40, "b": 20},
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font={"family": "Inter, sans-serif", "color": "#c9c9c9"},
+    )
+
+    return fig
+
+
+def create_summary_metrics(
+    metrics: dict[str, dict[str, Any]],
+) -> list[go.Figure]:
+    """Create multiple metric card figures.
+
+    Args:
+        metrics: Dictionary of metric configurations.
+            Key: metric name
+            Value: dict with 'value', 'title', 'delta' (optional), etc.
+
+    Returns:
+        List of Plotly Figure objects.
+    """
+    figures = []
+
+    for metric_config in metrics.values():
+        fig = create_metric_card_figure(
+            value=metric_config.get("value", 0),
+            title=metric_config.get("title", ""),
+            delta=metric_config.get("delta"),
+            delta_suffix=metric_config.get("delta_suffix", "%"),
+            prefix=metric_config.get("prefix", ""),
+            suffix=metric_config.get("suffix", ""),
+            format_spec=metric_config.get("format_spec", ",.0f"),
+            positive_is_good=metric_config.get("positive_is_good", True),
+        )
+        figures.append(fig)
+
+    return figures

portfolio_app/figures/time_series.py

@@ -0,0 +1,386 @@
+"""Time series figure factories for Toronto housing data."""
+
+from typing import Any
+
+import plotly.express as px
+import plotly.graph_objects as go
+
+
+def create_price_time_series(
+    data: list[dict[str, Any]],
+    date_column: str = "full_date",
+    price_column: str = "avg_price",
+    group_column: str | None = None,
+    title: str = "Average Price Over Time",
+    show_yoy: bool = True,
+) -> go.Figure:
+    """Create a time series chart for price data.
+
+    Args:
+        data: List of records with date and price columns.
+        date_column: Column name for dates.
+        price_column: Column name for price values.
+        group_column: Optional column for grouping (e.g., district_code).
+        title: Chart title.
+        show_yoy: Whether to show year-over-year change annotations.
+
+    Returns:
+        Plotly Figure object.
+    """
+    import pandas as pd
+
+    if not data:
+        fig = go.Figure()
+        fig.add_annotation(
+            text="No data available",
+            xref="paper",
+            yref="paper",
+            x=0.5,
+            y=0.5,
+            showarrow=False,
+            font={"color": "#888888"},
+        )
+        fig.update_layout(
+            title=title,
+            height=350,
+            paper_bgcolor="rgba(0,0,0,0)",
+            plot_bgcolor="rgba(0,0,0,0)",
+            font_color="#c9c9c9",
+        )
+        return fig
+
+    df = pd.DataFrame(data)
+    df[date_column] = pd.to_datetime(df[date_column])
+
+    if group_column and group_column in df.columns:
+        fig = px.line(
+            df,
+            x=date_column,
+            y=price_column,
+            color=group_column,
+            title=title,
+        )
+    else:
+        fig = px.line(
+            df,
+            x=date_column,
+            y=price_column,
+            title=title,
+        )
+
+    fig.update_layout(
+        height=350,
+        margin={"l": 40, "r": 20, "t": 50, "b": 40},
+        xaxis_title="Date",
+        yaxis_title=price_column.replace("_", " ").title(),
+        yaxis_tickprefix="$",
+        yaxis_tickformat=",",
+        hovermode="x unified",
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        yaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+    )
+
+    return fig
+
+
+def create_volume_time_series(
+    data: list[dict[str, Any]],
+    date_column: str = "full_date",
+    volume_column: str = "sales_count",
+    group_column: str | None = None,
+    title: str = "Sales Volume Over Time",
+    chart_type: str = "bar",
+) -> go.Figure:
+    """Create a time series chart for volume/count data.
+
+    Args:
+        data: List of records with date and volume columns.
+        date_column: Column name for dates.
+        volume_column: Column name for volume values.
+        group_column: Optional column for grouping.
+        title: Chart title.
+        chart_type: 'bar' or 'line'.
+
+    Returns:
+        Plotly Figure object.
+    """
+    import pandas as pd
+
+    if not data:
+        fig = go.Figure()
+        fig.add_annotation(
+            text="No data available",
+            xref="paper",
+            yref="paper",
+            x=0.5,
+            y=0.5,
+            showarrow=False,
+            font={"color": "#888888"},
+        )
+        fig.update_layout(
+            title=title,
+            height=350,
+            paper_bgcolor="rgba(0,0,0,0)",
+            plot_bgcolor="rgba(0,0,0,0)",
+            font_color="#c9c9c9",
+        )
+        return fig
+
+    df = pd.DataFrame(data)
+    df[date_column] = pd.to_datetime(df[date_column])
+
+    if chart_type == "bar":
+        if group_column and group_column in df.columns:
+            fig = px.bar(
+                df,
+                x=date_column,
+                y=volume_column,
+                color=group_column,
+                title=title,
+            )
+        else:
+            fig = px.bar(
+                df,
+                x=date_column,
+                y=volume_column,
+                title=title,
+            )
+    else:
+        if group_column and group_column in df.columns:
+            fig = px.line(
+                df,
+                x=date_column,
+                y=volume_column,
+                color=group_column,
+                title=title,
+            )
+        else:
+            fig = px.line(
+                df,
+                x=date_column,
+                y=volume_column,
+                title=title,
+            )
+
+    fig.update_layout(
+        height=350,
+        margin={"l": 40, "r": 20, "t": 50, "b": 40},
+        xaxis_title="Date",
+        yaxis_title=volume_column.replace("_", " ").title(),
+        yaxis_tickformat=",",
+        hovermode="x unified",
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        yaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+    )
+
+    return fig
+
+
+def create_market_comparison_chart(
+    data: list[dict[str, Any]],
+    date_column: str = "full_date",
+    metrics: list[str] | None = None,
+    title: str = "Market Indicators",
+) -> go.Figure:
+    """Create a multi-metric comparison chart.
+
+    Args:
+        data: List of records with date and metric columns.
+        date_column: Column name for dates.
+        metrics: List of metric columns to display.
+        title: Chart title.
+
+    Returns:
+        Plotly Figure object with secondary y-axis.
+    """
+    import pandas as pd
+    from plotly.subplots import make_subplots
+
+    if not data:
+        fig = go.Figure()
+        fig.add_annotation(
+            text="No data available",
+            xref="paper",
+            yref="paper",
+            x=0.5,
+            y=0.5,
+            showarrow=False,
+            font={"color": "#888888"},
+        )
+        fig.update_layout(
+            title=title,
+            height=400,
+            paper_bgcolor="rgba(0,0,0,0)",
+            plot_bgcolor="rgba(0,0,0,0)",
+            font_color="#c9c9c9",
+        )
+        return fig
+
+    if metrics is None:
+        metrics = ["avg_price", "sales_count"]
+
+    df = pd.DataFrame(data)
+    df[date_column] = pd.to_datetime(df[date_column])
+
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+
+    colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728"]
+
+    for i, metric in enumerate(metrics[:4]):
+        if metric not in df.columns:
+            continue
+
+        secondary = i > 0
+        fig.add_trace(
+            go.Scatter(
+                x=df[date_column],
+                y=df[metric],
+                name=metric.replace("_", " ").title(),
+                line={"color": colors[i % len(colors)]},
+            ),
+            secondary_y=secondary,
+        )
+
+    fig.update_layout(
+        title=title,
+        height=400,
+        margin={"l": 40, "r": 40, "t": 50, "b": 40},
+        hovermode="x unified",
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color="#c9c9c9",
+        xaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        yaxis={"gridcolor": "#333333", "linecolor": "#444444"},
+        legend={
+            "orientation": "h",
+            "yanchor": "bottom",
+            "y": 1.02,
+            "xanchor": "right",
+            "x": 1,
+            "font": {"color": "#c9c9c9"},
+        },
+    )
+
+    return fig
+
+
+def add_policy_markers(
+    fig: go.Figure,
+    policy_events: list[dict[str, Any]],
+    date_column: str = "event_date",
+    y_position: float | None = None,
+) -> go.Figure:
+    """Add policy event markers to an existing time series figure.
+
+    Args:
+        fig: Existing Plotly figure to add markers to.
+        policy_events: List of policy event dicts with date and metadata.
+        date_column: Column name for event dates.
+        y_position: Y position for markers. If None, uses top of chart.
+
+    Returns:
+        Updated Plotly Figure object with policy markers.
+    """
+    if not policy_events:
+        return fig
+
+    # Color mapping for policy categories
+    category_colors = {
+        "monetary": "#1f77b4",  # Blue
+        "tax": "#2ca02c",  # Green
+        "regulatory": "#ff7f0e",  # Orange
+        "supply": "#9467bd",  # Purple
+        "economic": "#d62728",  # Red
+    }
+
+    # Symbol mapping for expected direction
+    direction_symbols = {
+        "bullish": "triangle-up",
+        "bearish": "triangle-down",
+        "neutral": "circle",
+    }
+
+    for event in policy_events:
+        event_date = event.get(date_column)
+        category = event.get("category", "economic")
+        direction = event.get("expected_direction", "neutral")
+        title = event.get("title", "Policy Event")
+        level = event.get("level", "federal")
+
+        color = category_colors.get(category, "#666666")
+        symbol = direction_symbols.get(direction, "circle")
+
+        # Add vertical line for the event
+        fig.add_vline(
+            x=event_date,
+            line_dash="dot",
+            line_color=color,
+            opacity=0.5,
+            annotation_text="",
+        )
+
+        # Add marker with hover info
+        fig.add_trace(
+            go.Scatter(
+                x=[event_date],
+                y=[y_position] if y_position else [None],  # type: ignore[list-item]
+                mode="markers",
+                marker={
+                    "symbol": symbol,
+                    "size": 12,
+                    "color": color,
+                    "line": {"width": 1, "color": "white"},
+                },
+                name=title,
+                hovertemplate=(
+                    f"<b>{title}</b><br>"
+                    f"Date: %{{x}}<br>"
+                    f"Level: {level.title()}<br>"
+                    f"Category: {category.title()}<br>"
+                    f"<extra></extra>"
+                ),
+                showlegend=False,
+            )
+        )
+
+    return fig
+
+
+def create_time_series_with_events(
+    data: list[dict[str, Any]],
+    policy_events: list[dict[str, Any]],
+    date_column: str = "full_date",
+    value_column: str = "avg_price",
+    title: str = "Price Trend with Policy Events",
+) -> go.Figure:
+    """Create a time series chart with policy event markers.
+
+    Args:
+        data: Time series data.
+        policy_events: Policy events to overlay.
+        date_column: Column name for dates.
+        value_column: Column name for values.
+        title: Chart title.
+
+    Returns:
+        Plotly Figure with time series and policy markers.
+    """
+    # Create base time series
+    fig = create_price_time_series(
+        data=data,
+        date_column=date_column,
+        price_column=value_column,
+        title=title,
+    )
+
+    # Add policy markers at the top of the chart
+    if policy_events:
+        fig = add_policy_markers(fig, policy_events)
+
+    return fig

portfolio_app/pages/__init__.py

@@ -0,0 +1 @@
+"""Dash pages."""

portfolio_app/pages/health.py

@@ -0,0 +1,20 @@
+"""Health check endpoint for deployment monitoring."""
+
+import dash
+from dash import html
+
+dash.register_page(
+    __name__,
+    path="/health",
+    title="Health Check",
+)
+
+
+def layout() -> html.Div:
+    """Return simple health check response."""
+    return html.Div(
+        [
+            html.Pre("status: ok"),
+        ],
+        id="health-check",
+    )

portfolio_app/pages/home.py

@@ -0,0 +1,169 @@
+"""Bio landing page."""
+
+import dash
+import dash_mantine_components as dmc
+
+dash.register_page(__name__, path="/", name="Home")
+
+# Content from bio_content_v2.md
+HEADLINE = "Leo | Data Engineer & Analytics Developer"
+TAGLINE = "I build data infrastructure that actually gets used."
+
+SUMMARY = """Over the past 5 years, I've designed and evolved an enterprise analytics platform
+from scratch—now processing 1B+ rows across 21 tables with Python-based ETL pipelines and
+dbt-style SQL transformations. The result: 40% efficiency gains, 30% reduction in call
+abandon rates, and dashboards that executives actually open.
+
+My approach: dimensional modeling (star schema), layered transformations
+(staging → intermediate → marts), and automation that eliminates manual work.
+I've built everything from self-service analytics portals to OCR-powered receipt processing systems.
+
+Currently at Summitt Energy supporting multi-market operations across Canada and 8 US states.
+Previously cut my teeth on IT infrastructure projects at Petrobras (Fortune 500) and the
+Project Management Institute."""
+
+TECH_STACK = [
+    "Python",
+    "Pandas",
+    "SQLAlchemy",
+    "FastAPI",
+    "SQL",
+    "PostgreSQL",
+    "MSSQL",
+    "Power BI",
+    "Plotly/Dash",
+    "dbt patterns",
+    "Genesys Cloud",
+]
+
+PROJECTS = [
+    {
+        "title": "Toronto Housing Dashboard",
+        "description": "Choropleth visualization of GTA real estate trends with TRREB and CMHC data.",
+        "status": "In Development",
+        "link": "/toronto",
+    },
+    {
+        "title": "Energy Pricing Analysis",
+        "description": "Time series analysis and ML prediction for utility market pricing.",
+        "status": "Planned",
+        "link": "/energy",
+    },
+]
+
+AVAILABILITY = "Open to Senior Data Analyst, Analytics Engineer, and BI Developer opportunities in Toronto or remote."
+
+
+def create_hero_section() -> dmc.Stack:
+    """Create the hero section with name and tagline."""
+    return dmc.Stack(
+        [
+            dmc.Title(HEADLINE, order=1, ta="center"),
+            dmc.Text(TAGLINE, size="xl", c="dimmed", ta="center"),
+        ],
+        gap="xs",
+        py="xl",
+    )
+
+
+def create_summary_section() -> dmc.Paper:
+    """Create the professional summary section."""
+    paragraphs = SUMMARY.strip().split("\n\n")
+    return dmc.Paper(
+        dmc.Stack(
+            [
+                dmc.Title("About", order=2, size="h3"),
+                *[dmc.Text(p.replace("\n", " "), size="md") for p in paragraphs],
+            ],
+            gap="md",
+        ),
+        p="xl",
+        radius="md",
+        withBorder=True,
+    )
+
+
+def create_tech_stack_section() -> dmc.Paper:
+    """Create the tech stack section with badges."""
+    return dmc.Paper(
+        dmc.Stack(
+            [
+                dmc.Title("Tech Stack", order=2, size="h3"),
+                dmc.Group(
+                    [
+                        dmc.Badge(tech, size="lg", variant="light", radius="sm")
+                        for tech in TECH_STACK
+                    ],
+                    gap="sm",
+                ),
+            ],
+            gap="md",
+        ),
+        p="xl",
+        radius="md",
+        withBorder=True,
+    )
+
+
+def create_project_card(project: dict[str, str]) -> dmc.Card:
+    """Create a project card."""
+    status_color = "blue" if project["status"] == "In Development" else "gray"
+    return dmc.Card(
+        [
+            dmc.Group(
+                [
+                    dmc.Text(project["title"], fw=500, size="lg"),
+                    dmc.Badge(project["status"], color=status_color, variant="light"),
+                ],
+                justify="space-between",
+                align="center",
+            ),
+            dmc.Text(project["description"], size="sm", c="dimmed", mt="sm"),
+        ],
+        withBorder=True,
+        radius="md",
+        p="lg",
+    )
+
+
+def create_projects_section() -> dmc.Paper:
+    """Create the portfolio projects section."""
+    return dmc.Paper(
+        dmc.Stack(
+            [
+                dmc.Title("Portfolio Projects", order=2, size="h3"),
+                dmc.SimpleGrid(
+                    [create_project_card(p) for p in PROJECTS],
+                    cols={"base": 1, "sm": 2},
+                    spacing="lg",
+                ),
+            ],
+            gap="md",
+        ),
+        p="xl",
+        radius="md",
+        withBorder=True,
+    )
+
+
+def create_availability_section() -> dmc.Text:
+    """Create the availability statement."""
+    return dmc.Text(AVAILABILITY, size="sm", c="dimmed", ta="center", fs="italic")
+
+
+layout = dmc.Container(
+    dmc.Stack(
+        [
+            create_hero_section(),
+            create_summary_section(),
+            create_tech_stack_section(),
+            create_projects_section(),
+            dmc.Divider(my="lg"),
+            create_availability_section(),
+            dmc.Space(h=40),
+        ],
+        gap="xl",
+    ),
+    size="md",
+    py="xl",
+)

portfolio_app/pages/toronto/__init__.py

@@ -0,0 +1 @@
+"""Toronto Housing Dashboard pages."""

portfolio_app/pages/toronto/dashboard.py

@@ -0,0 +1,294 @@
+"""Toronto Housing Dashboard page."""
+
+import dash
+import dash_mantine_components as dmc
+from dash import dcc, html
+from dash_iconify import DashIconify
+
+from portfolio_app.components import (
+    create_map_controls,
+    create_metric_cards_row,
+    create_time_slider,
+    create_year_selector,
+)
+
+dash.register_page(__name__, path="/toronto", name="Toronto Housing")
+
+# Metric options for the purchase market
+PURCHASE_METRIC_OPTIONS = [
+    {"label": "Average Price", "value": "avg_price"},
+    {"label": "Median Price", "value": "median_price"},
+    {"label": "Sales Volume", "value": "sales_count"},
+    {"label": "Days on Market", "value": "avg_dom"},
+]
+
+# Metric options for the rental market
+RENTAL_METRIC_OPTIONS = [
+    {"label": "Average Rent", "value": "avg_rent"},
+    {"label": "Vacancy Rate", "value": "vacancy_rate"},
+    {"label": "Rental Universe", "value": "rental_universe"},
+]
+
+# Sample metrics for KPI cards (will be populated by callbacks)
+SAMPLE_METRICS = [
+    {
+        "title": "Avg. Price",
+        "value": 1125000,
+        "delta": 2.3,
+        "prefix": "$",
+        "format_spec": ",.0f",
+    },
+    {
+        "title": "Sales Volume",
+        "value": 4850,
+        "delta": -5.1,
+        "format_spec": ",",
+    },
+    {
+        "title": "Avg. DOM",
+        "value": 18,
+        "delta": 3,
+        "suffix": " days",
+        "positive_is_good": False,
+    },
+    {
+        "title": "Avg. Rent",
+        "value": 2450,
+        "delta": 4.2,
+        "prefix": "$",
+        "format_spec": ",.0f",
+    },
+]
+
+
+def create_header() -> dmc.Group:
+    """Create the dashboard header with title and controls."""
+    return dmc.Group(
+        [
+            dmc.Stack(
+                [
+                    dmc.Title("Toronto Housing Dashboard", order=1),
+                    dmc.Text(
+                        "Real estate market analysis for the Greater Toronto Area",
+                        c="dimmed",
+                    ),
+                ],
+                gap="xs",
+            ),
+            dmc.Group(
+                [
+                    dcc.Link(
+                        dmc.Button(
+                            "Methodology",
+                            leftSection=DashIconify(
+                                icon="tabler:info-circle", width=18
+                            ),
+                            variant="subtle",
+                            color="gray",
+                        ),
+                        href="/toronto/methodology",
+                    ),
+                    create_year_selector(
+                        id_prefix="toronto",
+                        min_year=2020,
+                        default_year=2024,
+                        label="Year",
+                    ),
+                ],
+                gap="md",
+            ),
+        ],
+        justify="space-between",
+        align="flex-start",
+    )
+
+
+def create_kpi_section() -> dmc.Box:
+    """Create the KPI metrics row."""
+    return dmc.Box(
+        children=[
+            dmc.Title("Key Metrics", order=3, size="h4", mb="sm"),
+            html.Div(
+                id="toronto-kpi-cards",
+                children=[
+                    create_metric_cards_row(SAMPLE_METRICS, id_prefix="toronto-kpi")
+                ],
+            ),
+        ],
+    )
+
+
+def create_purchase_map_section() -> dmc.Grid:
+    """Create the purchase market choropleth section."""
+    return dmc.Grid(
+        [
+            dmc.GridCol(
+                create_map_controls(
+                    id_prefix="purchase-map",
+                    metric_options=PURCHASE_METRIC_OPTIONS,
+                    default_metric="avg_price",
+                ),
+                span={"base": 12, "md": 3},
+            ),
+            dmc.GridCol(
+                dmc.Paper(
+                    children=[
+                        dcc.Graph(
+                            id="purchase-choropleth",
+                            config={"scrollZoom": True},
+                            style={"height": "500px"},
+                        ),
+                    ],
+                    p="xs",
+                    radius="sm",
+                    withBorder=True,
+                ),
+                span={"base": 12, "md": 9},
+            ),
+        ],
+        gutter="md",
+    )
+
+
+def create_rental_map_section() -> dmc.Grid:
+    """Create the rental market choropleth section."""
+    return dmc.Grid(
+        [
+            dmc.GridCol(
+                create_map_controls(
+                    id_prefix="rental-map",
+                    metric_options=RENTAL_METRIC_OPTIONS,
+                    default_metric="avg_rent",
+                ),
+                span={"base": 12, "md": 3},
+            ),
+            dmc.GridCol(
+                dmc.Paper(
+                    children=[
+                        dcc.Graph(
+                            id="rental-choropleth",
+                            config={"scrollZoom": True},
+                            style={"height": "500px"},
+                        ),
+                    ],
+                    p="xs",
+                    radius="sm",
+                    withBorder=True,
+                ),
+                span={"base": 12, "md": 9},
+            ),
+        ],
+        gutter="md",
+    )
+
+
+def create_time_series_section() -> dmc.Grid:
+    """Create the time series charts section."""
+    return dmc.Grid(
+        [
+            dmc.GridCol(
+                dmc.Paper(
+                    children=[
+                        dmc.Title("Price Trends", order=4, size="h5", mb="sm"),
+                        dcc.Graph(
+                            id="price-time-series",
+                            config={"displayModeBar": False},
+                            style={"height": "350px"},
+                        ),
+                    ],
+                    p="md",
+                    radius="sm",
+                    withBorder=True,
+                ),
+                span={"base": 12, "md": 6},
+            ),
+            dmc.GridCol(
+                dmc.Paper(
+                    children=[
+                        dmc.Title("Sales Volume", order=4, size="h5", mb="sm"),
+                        dcc.Graph(
+                            id="volume-time-series",
+                            config={"displayModeBar": False},
+                            style={"height": "350px"},
+                        ),
+                    ],
+                    p="md",
+                    radius="sm",
+                    withBorder=True,
+                ),
+                span={"base": 12, "md": 6},
+            ),
+        ],
+        gutter="md",
+    )
+
+
+def create_market_comparison_section() -> dmc.Paper:
+    """Create the market comparison chart section."""
+    return dmc.Paper(
+        children=[
+            dmc.Group(
+                [
+                    dmc.Title("Market Indicators", order=4, size="h5"),
+                    create_time_slider(
+                        id_prefix="market-comparison",
+                        min_year=2020,
+                        label="",
+                    ),
+                ],
+                justify="space-between",
+                align="center",
+                mb="md",
+            ),
+            dcc.Graph(
+                id="market-comparison-chart",
+                config={"displayModeBar": False},
+                style={"height": "400px"},
+            ),
+        ],
+        p="md",
+        radius="sm",
+        withBorder=True,
+    )
+
+
+def create_data_notice() -> dmc.Alert:
+    """Create a notice about data availability."""
+    return dmc.Alert(
+        children=[
+            dmc.Text(
+                "This dashboard uses TRREB and CMHC data. "
+                "Geographic boundaries require QGIS digitization to enable choropleth maps. "
+                "Sample data is shown below.",
+                size="sm",
+            ),
+        ],
+        title="Data Notice",
+        color="blue",
+        variant="light",
+    )
+
+
+# Register callbacks
+from portfolio_app.pages.toronto import callbacks  # noqa: E402, F401
+
+layout = dmc.Container(
+    dmc.Stack(
+        [
+            create_header(),
+            create_data_notice(),
+            create_kpi_section(),
+            dmc.Divider(my="md", label="Purchase Market", labelPosition="center"),
+            create_purchase_map_section(),
+            dmc.Divider(my="md", label="Rental Market", labelPosition="center"),
+            create_rental_map_section(),
+            dmc.Divider(my="md", label="Trends", labelPosition="center"),
+            create_time_series_section(),
+            create_market_comparison_section(),
+            dmc.Space(h=40),
+        ],
+        gap="lg",
+    ),
+    size="xl",
+    py="xl",
+)

portfolio_app/pages/toronto/methodology.py

@@ -0,0 +1,274 @@
+"""Methodology page for Toronto Housing Dashboard."""
+
+import dash
+import dash_mantine_components as dmc
+from dash import dcc, html
+from dash_iconify import DashIconify
+
+dash.register_page(
+    __name__,
+    path="/toronto/methodology",
+    title="Methodology | Toronto Housing Dashboard",
+    description="Data sources, methodology, and limitations for the Toronto Housing Dashboard",
+)
+
+
+def layout() -> dmc.Container:
+    """Render the methodology page layout."""
+    return dmc.Container(
+        size="md",
+        py="xl",
+        children=[
+            # Back to Dashboard button
+            dcc.Link(
+                dmc.Button(
+                    "Back to Dashboard",
+                    leftSection=DashIconify(icon="tabler:arrow-left", width=18),
+                    variant="subtle",
+                    color="gray",
+                ),
+                href="/toronto",
+            ),
+            # Header
+            dmc.Title("Methodology", order=1, mb="lg", mt="md"),
+            dmc.Text(
+                "This page documents the data sources, processing methodology, "
+                "and known limitations of the Toronto Housing Dashboard.",
+                size="lg",
+                c="dimmed",
+                mb="xl",
+            ),
+            # Data Sources Section
+            dmc.Paper(
+                p="lg",
+                radius="md",
+                withBorder=True,
+                mb="lg",
+                children=[
+                    dmc.Title("Data Sources", order=2, mb="md"),
+                    # TRREB
+                    dmc.Title("Purchase Data: TRREB", order=3, size="h4", mb="sm"),
+                    dmc.Text(
+                        [
+                            "The Toronto Regional Real Estate Board (TRREB) publishes monthly ",
+                            html.Strong("Market Watch"),
+                            " reports containing aggregate statistics for residential real estate "
+                            "transactions across the Greater Toronto Area.",
+                        ],
+                        mb="sm",
+                    ),
+                    dmc.List(
+                        [
+                            dmc.ListItem("Source: TRREB Market Watch Reports (PDF)"),
+                            dmc.ListItem("Geographic granularity: ~35 TRREB Districts"),
+                            dmc.ListItem("Temporal granularity: Monthly"),
+                            dmc.ListItem("Coverage: 2021-present"),
+                            dmc.ListItem(
+                                [
+                                    "Metrics: Sales count, average/median price, new listings, ",
+                                    "active listings, days on market, sale-to-list ratio",
+                                ]
+                            ),
+                        ],
+                        mb="md",
+                    ),
+                    dmc.Anchor(
+                        "TRREB Market Watch Archive",
+                        href="https://trreb.ca/market-data/market-watch/market-watch-archive/",
+                        target="_blank",
+                        mb="lg",
+                    ),
+                    # CMHC
+                    dmc.Title(
+                        "Rental Data: CMHC", order=3, size="h4", mb="sm", mt="md"
+                    ),
+                    dmc.Text(
+                        [
+                            "Canada Mortgage and Housing Corporation (CMHC) conducts the annual ",
+                            html.Strong("Rental Market Survey"),
+                            " providing rental market statistics for major urban centres.",
+                        ],
+                        mb="sm",
+                    ),
+                    dmc.List(
+                        [
+                            dmc.ListItem("Source: CMHC Rental Market Survey (Excel)"),
+                            dmc.ListItem(
+                                "Geographic granularity: ~20 CMHC Zones (Census Tract aligned)"
+                            ),
+                            dmc.ListItem(
+                                "Temporal granularity: Annual (October survey)"
+                            ),
+                            dmc.ListItem("Coverage: 2021-present"),
+                            dmc.ListItem(
+                                [
+                                    "Metrics: Average/median rent, vacancy rate, universe count, ",
+                                    "turnover rate, year-over-year rent change",
+                                ]
+                            ),
+                        ],
+                        mb="md",
+                    ),
+                    dmc.Anchor(
+                        "CMHC Housing Market Information Portal",
+                        href="https://www.cmhc-schl.gc.ca/professionals/housing-markets-data-and-research/housing-data/data-tables/rental-market",
+                        target="_blank",
+                    ),
+                ],
+            ),
+            # Geographic Considerations
+            dmc.Paper(
+                p="lg",
+                radius="md",
+                withBorder=True,
+                mb="lg",
+                children=[
+                    dmc.Title("Geographic Considerations", order=2, mb="md"),
+                    dmc.Alert(
+                        title="Important: Non-Aligned Geographies",
+                        color="yellow",
+                        mb="md",
+                        children=[
+                            "TRREB Districts and CMHC Zones do ",
+                            html.Strong("not"),
+                            " align geographically. They are displayed as separate layers and "
+                            "should not be directly compared at the sub-regional level.",
+                        ],
+                    ),
+                    dmc.Text(
+                        "The dashboard presents three geographic layers:",
+                        mb="sm",
+                    ),
+                    dmc.List(
+                        [
+                            dmc.ListItem(
+                                [
+                                    html.Strong("TRREB Districts (~35): "),
+                                    "Used for purchase/sales data visualization. "
+                                    "Districts are defined by TRREB and labeled with codes like W01, C01, E01.",
+                                ]
+                            ),
+                            dmc.ListItem(
+                                [
+                                    html.Strong("CMHC Zones (~20): "),
+                                    "Used for rental data visualization. "
+                                    "Zones are aligned with Census Tract boundaries.",
+                                ]
+                            ),
+                            dmc.ListItem(
+                                [
+                                    html.Strong("City Neighbourhoods (158): "),
+                                    "Reference overlay only. "
+                                    "These are official City of Toronto neighbourhood boundaries.",
+                                ]
+                            ),
+                        ],
+                    ),
+                ],
+            ),
+            # Policy Events
+            dmc.Paper(
+                p="lg",
+                radius="md",
+                withBorder=True,
+                mb="lg",
+                children=[
+                    dmc.Title("Policy Event Annotations", order=2, mb="md"),
+                    dmc.Text(
+                        "The time series charts include markers for significant policy events "
+                        "that may have influenced housing market conditions. These annotations are "
+                        "for contextual reference only.",
+                        mb="md",
+                    ),
+                    dmc.Alert(
+                        title="No Causation Claims",
+                        color="blue",
+                        children=[
+                            "The presence of a policy marker near a market trend change does ",
+                            html.Strong("not"),
+                            " imply causation. Housing markets are influenced by numerous factors "
+                            "beyond policy interventions.",
+                        ],
+                    ),
+                ],
+            ),
+            # Limitations
+            dmc.Paper(
+                p="lg",
+                radius="md",
+                withBorder=True,
+                mb="lg",
+                children=[
+                    dmc.Title("Limitations", order=2, mb="md"),
+                    dmc.List(
+                        [
+                            dmc.ListItem(
+                                [
+                                    html.Strong("Aggregate Data: "),
+                                    "All statistics are aggregates. Individual property characteristics, "
+                                    "condition, and micro-location are not reflected.",
+                                ]
+                            ),
+                            dmc.ListItem(
+                                [
+                                    html.Strong("Reporting Lag: "),
+                                    "TRREB data reflects closed transactions, which may lag market "
+                                    "conditions by 1-3 months. CMHC data is annual.",
+                                ]
+                            ),
+                            dmc.ListItem(
+                                [
+                                    html.Strong("Geographic Boundaries: "),
+                                    "TRREB district boundaries were manually digitized from reference maps "
+                                    "and may contain minor inaccuracies.",
+                                ]
+                            ),
+                            dmc.ListItem(
+                                [
+                                    html.Strong("Data Suppression: "),
+                                    "Some cells may be suppressed for confidentiality when transaction "
+                                    "counts are below thresholds.",
+                                ]
+                            ),
+                        ],
+                    ),
+                ],
+            ),
+            # Technical Implementation
+            dmc.Paper(
+                p="lg",
+                radius="md",
+                withBorder=True,
+                children=[
+                    dmc.Title("Technical Implementation", order=2, mb="md"),
+                    dmc.Text("This dashboard is built with:", mb="sm"),
+                    dmc.List(
+                        [
+                            dmc.ListItem("Python 3.11+ with Dash and Plotly"),
+                            dmc.ListItem("PostgreSQL with PostGIS for geospatial data"),
+                            dmc.ListItem("dbt for data transformation"),
+                            dmc.ListItem("Pydantic for data validation"),
+                            dmc.ListItem("SQLAlchemy 2.0 for database operations"),
+                        ],
+                        mb="md",
+                    ),
+                    dmc.Anchor(
+                        "View source code on GitHub",
+                        href="https://github.com/lmiranda/personal-portfolio",
+                        target="_blank",
+                    ),
+                ],
+            ),
+            # Back link
+            dmc.Group(
+                mt="xl",
+                children=[
+                    dmc.Anchor(
+                        "← Back to Dashboard",
+                        href="/toronto",
+                        size="lg",
+                    ),
+                ],
+            ),
+        ],
+    )

portfolio_app/toronto/__init__.py

@@ -0,0 +1 @@
+"""Toronto housing data logic."""

portfolio_app/toronto/demo_data.py

@@ -0,0 +1,257 @@
+"""Demo/sample data for testing the Toronto Housing Dashboard without full pipeline.
+
+This module provides synthetic data for development and demonstration purposes.
+Replace with real data from the database in production.
+"""
+
+from datetime import date
+from typing import Any
+
+
+def get_demo_districts() -> list[dict[str, Any]]:
+    """Return sample TRREB district data."""
+    return [
+        {"district_code": "W01", "district_name": "Long Branch", "area_type": "West"},
+        {"district_code": "W02", "district_name": "Mimico", "area_type": "West"},
+        {
+            "district_code": "W03",
+            "district_name": "Kingsway South",
+            "area_type": "West",
+        },
+        {"district_code": "W04", "district_name": "Edenbridge", "area_type": "West"},
+        {"district_code": "W05", "district_name": "Islington", "area_type": "West"},
+        {"district_code": "W06", "district_name": "Rexdale", "area_type": "West"},
+        {"district_code": "W07", "district_name": "Willowdale", "area_type": "West"},
+        {"district_code": "W08", "district_name": "York", "area_type": "West"},
+        {
+            "district_code": "C01",
+            "district_name": "Downtown Core",
+            "area_type": "Central",
+        },
+        {"district_code": "C02", "district_name": "Annex", "area_type": "Central"},
+        {
+            "district_code": "C03",
+            "district_name": "Forest Hill",
+            "area_type": "Central",
+        },
+        {
+            "district_code": "C04",
+            "district_name": "Lawrence Park",
+            "area_type": "Central",
+        },
+        {
+            "district_code": "C06",
+            "district_name": "Willowdale East",
+            "area_type": "Central",
+        },
+        {"district_code": "C07", "district_name": "Thornhill", "area_type": "Central"},
+        {"district_code": "C08", "district_name": "Waterfront", "area_type": "Central"},
+        {"district_code": "E01", "district_name": "Leslieville", "area_type": "East"},
+        {"district_code": "E02", "district_name": "The Beaches", "area_type": "East"},
+        {"district_code": "E03", "district_name": "Danforth", "area_type": "East"},
+        {"district_code": "E04", "district_name": "Birch Cliff", "area_type": "East"},
+        {"district_code": "E05", "district_name": "Scarborough", "area_type": "East"},
+    ]
+
+
+def get_demo_purchase_data() -> list[dict[str, Any]]:
+    """Return sample purchase data for time series visualization."""
+    import random
+
+    random.seed(42)
+    data = []
+
+    base_prices = {
+        "W01": 850000,
+        "C01": 1200000,
+        "E01": 950000,
+    }
+
+    for year in [2024, 2025]:
+        for month in range(1, 13):
+            if year == 2025 and month > 12:
+                break
+
+            for district, base_price in base_prices.items():
+                # Add some randomness and trend
+                trend = (year - 2024) * 12 + month
+                price_variation = random.uniform(-0.05, 0.05)
+                trend_factor = 1 + (trend * 0.002)  # Slight upward trend
+
+                avg_price = int(base_price * trend_factor * (1 + price_variation))
+                sales = random.randint(50, 200)
+
+                data.append(
+                    {
+                        "district_code": district,
+                        "full_date": date(year, month, 1),
+                        "year": year,
+                        "month": month,
+                        "avg_price": avg_price,
+                        "median_price": int(avg_price * 0.95),
+                        "sales_count": sales,
+                        "new_listings": int(sales * random.uniform(1.2, 1.8)),
+                        "active_listings": int(sales * random.uniform(2.0, 3.5)),
+                        "days_on_market": random.randint(15, 45),
+                        "sale_to_list_ratio": round(random.uniform(0.95, 1.05), 2),
+                    }
+                )
+
+    return data
+
+
+def get_demo_rental_data() -> list[dict[str, Any]]:
+    """Return sample rental data for visualization."""
+    data = []
+
+    zones = [
+        ("Zone01", "Downtown"),
+        ("Zone02", "Midtown"),
+        ("Zone03", "North York"),
+        ("Zone04", "Scarborough"),
+        ("Zone05", "Etobicoke"),
+    ]
+
+    bedroom_types = ["bachelor", "1_bedroom", "2_bedroom", "3_bedroom"]
+
+    base_rents = {
+        "bachelor": 1800,
+        "1_bedroom": 2200,
+        "2_bedroom": 2800,
+        "3_bedroom": 3400,
+    }
+
+    for year in [2021, 2022, 2023, 2024, 2025]:
+        for zone_code, zone_name in zones:
+            for bedroom in bedroom_types:
+                # Rental trend: ~5% increase per year
+                year_factor = 1 + ((year - 2021) * 0.05)
+                base_rent = base_rents[bedroom]
+
+                data.append(
+                    {
+                        "zone_code": zone_code,
+                        "zone_name": zone_name,
+                        "survey_year": year,
+                        "full_date": date(year, 10, 1),
+                        "bedroom_type": bedroom,
+                        "average_rent": int(base_rent * year_factor),
+                        "median_rent": int(base_rent * year_factor * 0.98),
+                        "vacancy_rate": round(
+                            2.5 - (year - 2021) * 0.3, 1
+                        ),  # Decreasing vacancy
+                        "universe": 5000 + (year - 2021) * 200,
+                    }
+                )
+
+    return data
+
+
+def get_demo_policy_events() -> list[dict[str, Any]]:
+    """Return sample policy events for annotation."""
+    return [
+        {
+            "event_date": date(2024, 6, 5),
+            "effective_date": date(2024, 6, 5),
+            "level": "federal",
+            "category": "monetary",
+            "title": "BoC Rate Cut (25bp)",
+            "description": "Bank of Canada cuts overnight rate by 25 basis points to 4.75%",
+            "expected_direction": "bullish",
+        },
+        {
+            "event_date": date(2024, 7, 24),
+            "effective_date": date(2024, 7, 24),
+            "level": "federal",
+            "category": "monetary",
+            "title": "BoC Rate Cut (25bp)",
+            "description": "Bank of Canada cuts overnight rate by 25 basis points to 4.50%",
+            "expected_direction": "bullish",
+        },
+        {
+            "event_date": date(2024, 9, 4),
+            "effective_date": date(2024, 9, 4),
+            "level": "federal",
+            "category": "monetary",
+            "title": "BoC Rate Cut (25bp)",
+            "description": "Bank of Canada cuts overnight rate by 25 basis points to 4.25%",
+            "expected_direction": "bullish",
+        },
+        {
+            "event_date": date(2024, 10, 23),
+            "effective_date": date(2024, 10, 23),
+            "level": "federal",
+            "category": "monetary",
+            "title": "BoC Rate Cut (50bp)",
+            "description": "Bank of Canada cuts overnight rate by 50 basis points to 3.75%",
+            "expected_direction": "bullish",
+        },
+        {
+            "event_date": date(2024, 12, 11),
+            "effective_date": date(2024, 12, 11),
+            "level": "federal",
+            "category": "monetary",
+            "title": "BoC Rate Cut (50bp)",
+            "description": "Bank of Canada cuts overnight rate by 50 basis points to 3.25%",
+            "expected_direction": "bullish",
+        },
+        {
+            "event_date": date(2024, 9, 16),
+            "effective_date": date(2024, 12, 15),
+            "level": "federal",
+            "category": "regulatory",
+            "title": "CMHC 30-Year Amortization",
+            "description": "30-year amortization extended to all first-time buyers and new builds",
+            "expected_direction": "bullish",
+        },
+        {
+            "event_date": date(2024, 9, 16),
+            "effective_date": date(2024, 12, 15),
+            "level": "federal",
+            "category": "regulatory",
+            "title": "Insured Mortgage Cap $1.5M",
+            "description": "Insured mortgage cap raised from $1M to $1.5M",
+            "expected_direction": "bullish",
+        },
+    ]
+
+
+def get_demo_summary_metrics() -> dict[str, dict[str, Any]]:
+    """Return summary metrics for KPI cards."""
+    return {
+        "avg_price": {
+            "value": 1067968,
+            "title": "Avg. Price (2025)",
+            "delta": -4.7,
+            "delta_suffix": "%",
+            "prefix": "$",
+            "format_spec": ",.0f",
+            "positive_is_good": True,
+        },
+        "total_sales": {
+            "value": 67610,
+            "title": "Total Sales (2024)",
+            "delta": 2.6,
+            "delta_suffix": "%",
+            "format_spec": ",.0f",
+            "positive_is_good": True,
+        },
+        "avg_rent": {
+            "value": 2450,
+            "title": "Avg. Rent (2025)",
+            "delta": 3.2,
+            "delta_suffix": "%",
+            "prefix": "$",
+            "format_spec": ",.0f",
+            "positive_is_good": False,
+        },
+        "vacancy_rate": {
+            "value": 1.8,
+            "title": "Vacancy Rate",
+            "delta": -0.4,
+            "delta_suffix": "pp",
+            "suffix": "%",
+            "format_spec": ".1f",
+            "positive_is_good": False,
+        },
+    }

portfolio_app/toronto/loaders/__init__.py

@@ -0,0 +1,32 @@
+"""Database loaders for Toronto housing data."""
+
+from .base import bulk_insert, get_session, upsert_by_key
+from .cmhc import load_cmhc_record, load_cmhc_rentals
+from .dimensions import (
+    generate_date_key,
+    load_cmhc_zones,
+    load_neighbourhoods,
+    load_policy_events,
+    load_time_dimension,
+    load_trreb_districts,
+)
+from .trreb import load_trreb_purchases, load_trreb_record
+
+__all__ = [
+    # Base utilities
+    "get_session",
+    "bulk_insert",
+    "upsert_by_key",
+    # Dimension loaders
+    "generate_date_key",
+    "load_time_dimension",
+    "load_trreb_districts",
+    "load_cmhc_zones",
+    "load_neighbourhoods",
+    "load_policy_events",
+    # Fact loaders
+    "load_trreb_purchases",
+    "load_trreb_record",
+    "load_cmhc_rentals",
+    "load_cmhc_record",
+]

portfolio_app/toronto/loaders/base.py

@@ -0,0 +1,85 @@
+"""Base loader utilities for database operations."""
+
+from collections.abc import Generator
+from contextlib import contextmanager
+from typing import Any, TypeVar
+
+from sqlalchemy.orm import Session
+
+from portfolio_app.toronto.models import get_session_factory
+
+T = TypeVar("T")
+
+
+@contextmanager
+def get_session() -> Generator[Session, None, None]:
+    """Get a database session with automatic cleanup.
+
+    Yields:
+        SQLAlchemy session that auto-commits on success, rollbacks on error.
+    """
+    session_factory = get_session_factory()
+    session = session_factory()
+    try:
+        yield session
+        session.commit()
+    except Exception:
+        session.rollback()
+        raise
+    finally:
+        session.close()
+
+
+def bulk_insert(session: Session, objects: list[T]) -> int:
+    """Bulk insert objects into the database.
+
+    Args:
+        session: Active SQLAlchemy session.
+        objects: List of ORM model instances to insert.
+
+    Returns:
+        Number of objects inserted.
+    """
+    session.add_all(objects)
+    session.flush()
+    return len(objects)
+
+
+def upsert_by_key(
+    session: Session,
+    model_class: Any,
+    objects: list[T],
+    key_columns: list[str],
+) -> tuple[int, int]:
+    """Upsert objects based on unique key columns.
+
+    Args:
+        session: Active SQLAlchemy session.
+        model_class: The ORM model class.
+        objects: List of ORM model instances to upsert.
+        key_columns: Column names that form the unique key.
+
+    Returns:
+        Tuple of (inserted_count, updated_count).
+    """
+    inserted = 0
+    updated = 0
+
+    for obj in objects:
+        # Build filter for existing record
+        filters = {col: getattr(obj, col) for col in key_columns}
+        existing = session.query(model_class).filter_by(**filters).first()
+
+        if existing:
+            # Update existing record
+            for column in model_class.__table__.columns:
+                if column.name not in key_columns and column.name != "id":
+                    setattr(existing, column.name, getattr(obj, column.name))
+            updated += 1
+        else:
+            # Insert new record
+            session.add(obj)
+            inserted += 1
+
+    session.flush()
+    return inserted, updated