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>

.pre-commit-config.yaml

@@ -7,6 +7,7 @@ repos:
       - 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

CLAUDE.md

@@ -6,7 +6,7 @@ Working context for Claude Code on the Analytics Portfolio project.
 
 ## Project Status
 
-**Current Sprint**: 1 (Project Bootstrap)
+**Current Sprint**: 7 (Navigation & Theme Modernization)
 **Phase**: 1 - Toronto Housing Dashboard
 **Branch**: `development` (feature branches merge here)
 
@@ -254,4 +254,4 @@ All scripts in `scripts/`:
 
 ---
 
-*Last Updated: Sprint 1*
+*Last Updated: Sprint 7*

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

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*

portfolio_app/app.py

@@ -1,28 +1,49 @@
 """Dash application factory with Pages routing."""
 
 import dash
-from dash import html
+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."""
-    settings = get_settings()
-
     app = dash.Dash(
         __name__,
         use_pages=True,
         suppress_callback_exceptions=True,
         title="Analytics Portfolio",
+        external_stylesheets=dmc.styles.ALL,
     )
 
-    app.layout = html.Div(
-        [
-            dash.page_container,
-        ]
+    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
 
 

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

@@ -5,7 +5,7 @@ from functools import lru_cache
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
 
-class Settings(BaseSettings):
+class Settings(BaseSettings):  # type: ignore[misc]
     """Application settings loaded from environment variables."""
 
     model_config = SettingsConfigDict(

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/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

@@ -1,14 +1,169 @@
 """Bio landing page."""
 
 import dash
-from dash import html
+import dash_mantine_components as dmc
 
 dash.register_page(__name__, path="/", name="Home")
 
-layout = html.Div(
-    [
-        html.H1("Analytics Portfolio"),
-        html.P("Welcome to Leo's analytics portfolio."),
-        html.P("Dashboard coming soon."),
-    ]
+# 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

@@ -1 +1 @@
-"""Toronto Housing Dashboard page."""
+"""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/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

@@ -1 +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

portfolio_app/toronto/loaders/cmhc.py

@@ -0,0 +1,137 @@
+"""Loader for CMHC rental data into fact_rentals."""
+
+from sqlalchemy.orm import Session
+
+from portfolio_app.toronto.models import DimCMHCZone, DimTime, FactRentals
+from portfolio_app.toronto.schemas import CMHCAnnualSurvey, CMHCRentalRecord
+
+from .base import get_session, upsert_by_key
+from .dimensions import generate_date_key
+
+
+def load_cmhc_rentals(
+    survey: CMHCAnnualSurvey,
+    session: Session | None = None,
+) -> int:
+    """Load CMHC annual survey data into fact_rentals.
+
+    Args:
+        survey: Validated CMHC annual survey containing records.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded.
+    """
+    from datetime import date
+
+    def _load(sess: Session) -> int:
+        # Get zone key mapping
+        zones = sess.query(DimCMHCZone).all()
+        zone_map = {z.zone_code: z.zone_key for z in zones}
+
+        # CMHC surveys are annual - use October 1st as reference date
+        survey_date = date(survey.survey_year, 10, 1)
+        date_key = generate_date_key(survey_date)
+
+        # Verify time dimension exists
+        time_dim = sess.query(DimTime).filter_by(date_key=date_key).first()
+        if not time_dim:
+            raise ValueError(
+                f"Time dimension not found for date_key {date_key}. "
+                "Load time dimension first."
+            )
+
+        records = []
+        for record in survey.records:
+            zone_key = zone_map.get(record.zone_code)
+            if not zone_key:
+                # Skip records for unknown zones
+                continue
+
+            fact = FactRentals(
+                date_key=date_key,
+                zone_key=zone_key,
+                bedroom_type=record.bedroom_type.value,
+                universe=record.universe,
+                avg_rent=record.average_rent,
+                median_rent=record.median_rent,
+                vacancy_rate=record.vacancy_rate,
+                availability_rate=record.availability_rate,
+                turnover_rate=record.turnover_rate,
+                rent_change_pct=record.rent_change_pct,
+                reliability_code=record.average_rent_reliability.value
+                if record.average_rent_reliability
+                else None,
+            )
+            records.append(fact)
+
+        inserted, updated = upsert_by_key(
+            sess, FactRentals, records, ["date_key", "zone_key", "bedroom_type"]
+        )
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)
+
+
+def load_cmhc_record(
+    record: CMHCRentalRecord,
+    survey_year: int,
+    session: Session | None = None,
+) -> int:
+    """Load a single CMHC record into fact_rentals.
+
+    Args:
+        record: Single validated CMHC rental record.
+        survey_year: Year of the survey.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded (0 or 1).
+    """
+    from datetime import date
+
+    def _load(sess: Session) -> int:
+        # Get zone key
+        zone = sess.query(DimCMHCZone).filter_by(zone_code=record.zone_code).first()
+        if not zone:
+            return 0
+
+        survey_date = date(survey_year, 10, 1)
+        date_key = generate_date_key(survey_date)
+
+        # Verify time dimension exists
+        time_dim = sess.query(DimTime).filter_by(date_key=date_key).first()
+        if not time_dim:
+            raise ValueError(
+                f"Time dimension not found for date_key {date_key}. "
+                "Load time dimension first."
+            )
+
+        fact = FactRentals(
+            date_key=date_key,
+            zone_key=zone.zone_key,
+            bedroom_type=record.bedroom_type.value,
+            universe=record.universe,
+            avg_rent=record.average_rent,
+            median_rent=record.median_rent,
+            vacancy_rate=record.vacancy_rate,
+            availability_rate=record.availability_rate,
+            turnover_rate=record.turnover_rate,
+            rent_change_pct=record.rent_change_pct,
+            reliability_code=record.average_rent_reliability.value
+            if record.average_rent_reliability
+            else None,
+        )
+
+        inserted, updated = upsert_by_key(
+            sess, FactRentals, [fact], ["date_key", "zone_key", "bedroom_type"]
+        )
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)

portfolio_app/toronto/loaders/dimensions.py

@@ -0,0 +1,251 @@
+"""Loaders for dimension tables."""
+
+from datetime import date
+
+from sqlalchemy.orm import Session
+
+from portfolio_app.toronto.models import (
+    DimCMHCZone,
+    DimNeighbourhood,
+    DimPolicyEvent,
+    DimTime,
+    DimTRREBDistrict,
+)
+from portfolio_app.toronto.schemas import (
+    CMHCZone,
+    Neighbourhood,
+    PolicyEvent,
+    TRREBDistrict,
+)
+
+from .base import get_session, upsert_by_key
+
+
+def generate_date_key(d: date) -> int:
+    """Generate integer date key from date (YYYYMMDD format).
+
+    Args:
+        d: Date to convert.
+
+    Returns:
+        Integer in YYYYMMDD format.
+    """
+    return d.year * 10000 + d.month * 100 + d.day
+
+
+def load_time_dimension(
+    start_date: date,
+    end_date: date,
+    session: Session | None = None,
+) -> int:
+    """Load time dimension with date range.
+
+    Args:
+        start_date: Start of date range.
+        end_date: End of date range (inclusive).
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded.
+    """
+
+    month_names = [
+        "",
+        "January",
+        "February",
+        "March",
+        "April",
+        "May",
+        "June",
+        "July",
+        "August",
+        "September",
+        "October",
+        "November",
+        "December",
+    ]
+
+    def _load(sess: Session) -> int:
+        records = []
+        current = start_date.replace(day=1)  # Start at month beginning
+
+        while current <= end_date:
+            quarter = (current.month - 1) // 3 + 1
+            dim = DimTime(
+                date_key=generate_date_key(current),
+                full_date=current,
+                year=current.year,
+                month=current.month,
+                quarter=quarter,
+                month_name=month_names[current.month],
+                is_month_start=True,
+            )
+            records.append(dim)
+
+            # Move to next month
+            if current.month == 12:
+                current = current.replace(year=current.year + 1, month=1)
+            else:
+                current = current.replace(month=current.month + 1)
+
+        inserted, updated = upsert_by_key(sess, DimTime, records, ["date_key"])
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)
+
+
+def load_trreb_districts(
+    districts: list[TRREBDistrict],
+    session: Session | None = None,
+) -> int:
+    """Load TRREB district dimension.
+
+    Args:
+        districts: List of validated district schemas.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded.
+    """
+
+    def _load(sess: Session) -> int:
+        records = []
+        for d in districts:
+            dim = DimTRREBDistrict(
+                district_code=d.district_code,
+                district_name=d.district_name,
+                area_type=d.area_type.value,
+                geometry=d.geometry_wkt,
+            )
+            records.append(dim)
+
+        inserted, updated = upsert_by_key(
+            sess, DimTRREBDistrict, records, ["district_code"]
+        )
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)
+
+
+def load_cmhc_zones(
+    zones: list[CMHCZone],
+    session: Session | None = None,
+) -> int:
+    """Load CMHC zone dimension.
+
+    Args:
+        zones: List of validated zone schemas.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded.
+    """
+
+    def _load(sess: Session) -> int:
+        records = []
+        for z in zones:
+            dim = DimCMHCZone(
+                zone_code=z.zone_code,
+                zone_name=z.zone_name,
+                geometry=z.geometry_wkt,
+            )
+            records.append(dim)
+
+        inserted, updated = upsert_by_key(sess, DimCMHCZone, records, ["zone_code"])
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)
+
+
+def load_neighbourhoods(
+    neighbourhoods: list[Neighbourhood],
+    session: Session | None = None,
+) -> int:
+    """Load neighbourhood dimension.
+
+    Args:
+        neighbourhoods: List of validated neighbourhood schemas.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded.
+    """
+
+    def _load(sess: Session) -> int:
+        records = []
+        for n in neighbourhoods:
+            dim = DimNeighbourhood(
+                neighbourhood_id=n.neighbourhood_id,
+                name=n.name,
+                geometry=n.geometry_wkt,
+                population=n.population,
+                land_area_sqkm=n.land_area_sqkm,
+                pop_density_per_sqkm=n.pop_density_per_sqkm,
+                pct_bachelors_or_higher=n.pct_bachelors_or_higher,
+                median_household_income=n.median_household_income,
+                pct_owner_occupied=n.pct_owner_occupied,
+                pct_renter_occupied=n.pct_renter_occupied,
+                census_year=n.census_year,
+            )
+            records.append(dim)
+
+        inserted, updated = upsert_by_key(
+            sess, DimNeighbourhood, records, ["neighbourhood_id"]
+        )
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)
+
+
+def load_policy_events(
+    events: list[PolicyEvent],
+    session: Session | None = None,
+) -> int:
+    """Load policy event dimension.
+
+    Args:
+        events: List of validated policy event schemas.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded.
+    """
+
+    def _load(sess: Session) -> int:
+        records = []
+        for e in events:
+            dim = DimPolicyEvent(
+                event_date=e.event_date,
+                effective_date=e.effective_date,
+                level=e.level.value,
+                category=e.category.value,
+                title=e.title,
+                description=e.description,
+                expected_direction=e.expected_direction.value,
+                source_url=e.source_url,
+                confidence=e.confidence.value,
+            )
+            records.append(dim)
+
+        # For policy events, use event_date + title as unique key
+        inserted, updated = upsert_by_key(
+            sess, DimPolicyEvent, records, ["event_date", "title"]
+        )
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)

portfolio_app/toronto/loaders/trreb.py

@@ -0,0 +1,129 @@
+"""Loader for TRREB purchase data into fact_purchases."""
+
+from sqlalchemy.orm import Session
+
+from portfolio_app.toronto.models import DimTime, DimTRREBDistrict, FactPurchases
+from portfolio_app.toronto.schemas import TRREBMonthlyRecord, TRREBMonthlyReport
+
+from .base import get_session, upsert_by_key
+from .dimensions import generate_date_key
+
+
+def load_trreb_purchases(
+    report: TRREBMonthlyReport,
+    session: Session | None = None,
+) -> int:
+    """Load TRREB monthly report data into fact_purchases.
+
+    Args:
+        report: Validated TRREB monthly report containing records.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded.
+    """
+
+    def _load(sess: Session) -> int:
+        # Get district key mapping
+        districts = sess.query(DimTRREBDistrict).all()
+        district_map = {d.district_code: d.district_key for d in districts}
+
+        # Build date key from report date
+        date_key = generate_date_key(report.report_date)
+
+        # Verify time dimension exists
+        time_dim = sess.query(DimTime).filter_by(date_key=date_key).first()
+        if not time_dim:
+            raise ValueError(
+                f"Time dimension not found for date_key {date_key}. "
+                "Load time dimension first."
+            )
+
+        records = []
+        for record in report.records:
+            district_key = district_map.get(record.area_code)
+            if not district_key:
+                # Skip records for unknown districts (e.g., aggregate rows)
+                continue
+
+            fact = FactPurchases(
+                date_key=date_key,
+                district_key=district_key,
+                sales_count=record.sales,
+                dollar_volume=record.dollar_volume,
+                avg_price=record.avg_price,
+                median_price=record.median_price,
+                new_listings=record.new_listings,
+                active_listings=record.active_listings,
+                avg_dom=record.avg_dom,
+                avg_sp_lp=record.avg_sp_lp,
+            )
+            records.append(fact)
+
+        inserted, updated = upsert_by_key(
+            sess, FactPurchases, records, ["date_key", "district_key"]
+        )
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)
+
+
+def load_trreb_record(
+    record: TRREBMonthlyRecord,
+    session: Session | None = None,
+) -> int:
+    """Load a single TRREB record into fact_purchases.
+
+    Args:
+        record: Single validated TRREB monthly record.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded (0 or 1).
+    """
+
+    def _load(sess: Session) -> int:
+        # Get district key
+        district = (
+            sess.query(DimTRREBDistrict)
+            .filter_by(district_code=record.area_code)
+            .first()
+        )
+        if not district:
+            return 0
+
+        date_key = generate_date_key(record.report_date)
+
+        # Verify time dimension exists
+        time_dim = sess.query(DimTime).filter_by(date_key=date_key).first()
+        if not time_dim:
+            raise ValueError(
+                f"Time dimension not found for date_key {date_key}. "
+                "Load time dimension first."
+            )
+
+        fact = FactPurchases(
+            date_key=date_key,
+            district_key=district.district_key,
+            sales_count=record.sales,
+            dollar_volume=record.dollar_volume,
+            avg_price=record.avg_price,
+            median_price=record.median_price,
+            new_listings=record.new_listings,
+            active_listings=record.active_listings,
+            avg_dom=record.avg_dom,
+            avg_sp_lp=record.avg_sp_lp,
+        )
+
+        inserted, updated = upsert_by_key(
+            sess, FactPurchases, [fact], ["date_key", "district_key"]
+        )
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)

portfolio_app/toronto/models/__init__.py

@@ -1 +1,28 @@
 """SQLAlchemy models for Toronto housing data."""
+
+from .base import Base, create_tables, get_engine, get_session_factory
+from .dimensions import (
+    DimCMHCZone,
+    DimNeighbourhood,
+    DimPolicyEvent,
+    DimTime,
+    DimTRREBDistrict,
+)
+from .facts import FactPurchases, FactRentals
+
+__all__ = [
+    # Base
+    "Base",
+    "get_engine",
+    "get_session_factory",
+    "create_tables",
+    # Dimensions
+    "DimTime",
+    "DimTRREBDistrict",
+    "DimCMHCZone",
+    "DimNeighbourhood",
+    "DimPolicyEvent",
+    # Facts
+    "FactPurchases",
+    "FactRentals",
+]

portfolio_app/toronto/models/base.py

@@ -0,0 +1,30 @@
+"""SQLAlchemy base configuration and engine setup."""
+
+from sqlalchemy import Engine, create_engine
+from sqlalchemy.orm import DeclarativeBase, Session, sessionmaker
+
+from portfolio_app.config import get_settings
+
+
+class Base(DeclarativeBase):  # type: ignore[misc]
+    """Base class for all SQLAlchemy models."""
+
+    pass
+
+
+def get_engine() -> Engine:
+    """Create database engine from settings."""
+    settings = get_settings()
+    return create_engine(settings.database_url, echo=False)
+
+
+def get_session_factory() -> sessionmaker[Session]:
+    """Create session factory."""
+    engine = get_engine()
+    return sessionmaker(bind=engine)
+
+
+def create_tables() -> None:
+    """Create all tables in database."""
+    engine = get_engine()
+    Base.metadata.create_all(engine)

portfolio_app/toronto/models/dimensions.py

@@ -0,0 +1,104 @@
+"""SQLAlchemy models for dimension tables."""
+
+from datetime import date
+
+from geoalchemy2 import Geometry
+from sqlalchemy import Boolean, Date, Integer, Numeric, String, Text
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class DimTime(Base):
+    """Time dimension table."""
+
+    __tablename__ = "dim_time"
+
+    date_key: Mapped[int] = mapped_column(Integer, primary_key=True)
+    full_date: Mapped[date] = mapped_column(Date, nullable=False, unique=True)
+    year: Mapped[int] = mapped_column(Integer, nullable=False)
+    month: Mapped[int] = mapped_column(Integer, nullable=False)
+    quarter: Mapped[int] = mapped_column(Integer, nullable=False)
+    month_name: Mapped[str] = mapped_column(String(20), nullable=False)
+    is_month_start: Mapped[bool] = mapped_column(Boolean, default=True)
+
+
+class DimTRREBDistrict(Base):
+    """TRREB district dimension table with PostGIS geometry."""
+
+    __tablename__ = "dim_trreb_district"
+
+    district_key: Mapped[int] = mapped_column(
+        Integer, primary_key=True, autoincrement=True
+    )
+    district_code: Mapped[str] = mapped_column(String(3), nullable=False, unique=True)
+    district_name: Mapped[str] = mapped_column(String(100), nullable=False)
+    area_type: Mapped[str] = mapped_column(String(10), nullable=False)
+    geometry = mapped_column(Geometry("POLYGON", srid=4326), nullable=True)
+
+
+class DimCMHCZone(Base):
+    """CMHC zone dimension table with PostGIS geometry."""
+
+    __tablename__ = "dim_cmhc_zone"
+
+    zone_key: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    zone_code: Mapped[str] = mapped_column(String(10), nullable=False, unique=True)
+    zone_name: Mapped[str] = mapped_column(String(100), nullable=False)
+    geometry = mapped_column(Geometry("POLYGON", srid=4326), nullable=True)
+
+
+class DimNeighbourhood(Base):
+    """City of Toronto neighbourhood dimension.
+
+    Note: No FK to fact tables in V1 - reference overlay only.
+    """
+
+    __tablename__ = "dim_neighbourhood"
+
+    neighbourhood_id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    name: Mapped[str] = mapped_column(String(100), nullable=False)
+    geometry = mapped_column(Geometry("POLYGON", srid=4326), nullable=True)
+    population: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    land_area_sqkm: Mapped[float | None] = mapped_column(Numeric(10, 4), nullable=True)
+    pop_density_per_sqkm: Mapped[float | None] = mapped_column(
+        Numeric(10, 2), nullable=True
+    )
+    pct_bachelors_or_higher: Mapped[float | None] = mapped_column(
+        Numeric(5, 2), nullable=True
+    )
+    median_household_income: Mapped[float | None] = mapped_column(
+        Numeric(12, 2), nullable=True
+    )
+    pct_owner_occupied: Mapped[float | None] = mapped_column(
+        Numeric(5, 2), nullable=True
+    )
+    pct_renter_occupied: Mapped[float | None] = mapped_column(
+        Numeric(5, 2), nullable=True
+    )
+    census_year: Mapped[int] = mapped_column(Integer, default=2021)
+
+
+class DimPolicyEvent(Base):
+    """Policy event dimension for time-series annotation."""
+
+    __tablename__ = "dim_policy_event"
+
+    event_id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    event_date: Mapped[date] = mapped_column(Date, nullable=False)
+    effective_date: Mapped[date | None] = mapped_column(Date, nullable=True)
+    level: Mapped[str] = mapped_column(
+        String(20), nullable=False
+    )  # federal/provincial/municipal
+    category: Mapped[str] = mapped_column(
+        String(20), nullable=False
+    )  # monetary/tax/regulatory/supply/economic
+    title: Mapped[str] = mapped_column(String(200), nullable=False)
+    description: Mapped[str | None] = mapped_column(Text, nullable=True)
+    expected_direction: Mapped[str] = mapped_column(
+        String(10), nullable=False
+    )  # bearish/bullish/neutral
+    source_url: Mapped[str | None] = mapped_column(String(500), nullable=True)
+    confidence: Mapped[str] = mapped_column(
+        String(10), default="medium"
+    )  # high/medium/low

portfolio_app/toronto/models/facts.py

@@ -0,0 +1,69 @@
+"""SQLAlchemy models for fact tables."""
+
+from sqlalchemy import ForeignKey, Integer, Numeric, String
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from .base import Base
+
+
+class FactPurchases(Base):
+    """Fact table for TRREB purchase/sales data.
+
+    Grain: One row per district per month.
+    """
+
+    __tablename__ = "fact_purchases"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    date_key: Mapped[int] = mapped_column(
+        Integer, ForeignKey("dim_time.date_key"), nullable=False
+    )
+    district_key: Mapped[int] = mapped_column(
+        Integer, ForeignKey("dim_trreb_district.district_key"), nullable=False
+    )
+    sales_count: Mapped[int] = mapped_column(Integer, nullable=False)
+    dollar_volume: Mapped[float] = mapped_column(Numeric(15, 2), nullable=False)
+    avg_price: Mapped[float] = mapped_column(Numeric(12, 2), nullable=False)
+    median_price: Mapped[float] = mapped_column(Numeric(12, 2), nullable=False)
+    new_listings: Mapped[int] = mapped_column(Integer, nullable=False)
+    active_listings: Mapped[int] = mapped_column(Integer, nullable=False)
+    avg_dom: Mapped[int] = mapped_column(Integer, nullable=False)  # Days on market
+    avg_sp_lp: Mapped[float] = mapped_column(
+        Numeric(5, 2), nullable=False
+    )  # Sale/List ratio
+
+    # Relationships
+    time = relationship("DimTime", backref="purchases")
+    district = relationship("DimTRREBDistrict", backref="purchases")
+
+
+class FactRentals(Base):
+    """Fact table for CMHC rental market data.
+
+    Grain: One row per zone per bedroom type per survey year.
+    """
+
+    __tablename__ = "fact_rentals"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    date_key: Mapped[int] = mapped_column(
+        Integer, ForeignKey("dim_time.date_key"), nullable=False
+    )
+    zone_key: Mapped[int] = mapped_column(
+        Integer, ForeignKey("dim_cmhc_zone.zone_key"), nullable=False
+    )
+    bedroom_type: Mapped[str] = mapped_column(String(20), nullable=False)
+    universe: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    avg_rent: Mapped[float | None] = mapped_column(Numeric(10, 2), nullable=True)
+    median_rent: Mapped[float | None] = mapped_column(Numeric(10, 2), nullable=True)
+    vacancy_rate: Mapped[float | None] = mapped_column(Numeric(5, 2), nullable=True)
+    availability_rate: Mapped[float | None] = mapped_column(
+        Numeric(5, 2), nullable=True
+    )
+    turnover_rate: Mapped[float | None] = mapped_column(Numeric(5, 2), nullable=True)
+    rent_change_pct: Mapped[float | None] = mapped_column(Numeric(5, 2), nullable=True)
+    reliability_code: Mapped[str | None] = mapped_column(String(2), nullable=True)
+
+    # Relationships
+    time = relationship("DimTime", backref="rentals")
+    zone = relationship("DimCMHCZone", backref="rentals")

portfolio_app/toronto/parsers/__init__.py

@@ -1 +1,20 @@
-"""Data parsers for Toronto housing data sources."""
+"""Parsers for Toronto housing data sources."""
+
+from .cmhc import CMHCParser
+from .geo import (
+    CMHCZoneParser,
+    NeighbourhoodParser,
+    TRREBDistrictParser,
+    load_geojson,
+)
+from .trreb import TRREBParser
+
+__all__ = [
+    "TRREBParser",
+    "CMHCParser",
+    # GeoJSON parsers
+    "CMHCZoneParser",
+    "TRREBDistrictParser",
+    "NeighbourhoodParser",
+    "load_geojson",
+]

portfolio_app/toronto/parsers/cmhc.py

@@ -0,0 +1,147 @@
+"""CMHC CSV processor for rental market survey data.
+
+This module provides the structure for processing CMHC (Canada Mortgage and Housing
+Corporation) rental market survey data from CSV exports.
+"""
+
+from pathlib import Path
+from typing import Any, cast
+
+import pandas as pd
+
+from portfolio_app.toronto.schemas import CMHCAnnualSurvey, CMHCRentalRecord
+
+
+class CMHCParser:
+    """Parser for CMHC Rental Market Survey CSV data.
+
+    CMHC conducts annual rental market surveys and publishes data including:
+    - Average and median rents by zone and bedroom type
+    - Vacancy rates
+    - Universe (total rental units)
+    - Year-over-year rent changes
+
+    Data is available via the Housing Market Information Portal as CSV exports.
+    """
+
+    # Expected columns in CMHC CSV exports
+    REQUIRED_COLUMNS = {
+        "zone_code",
+        "zone_name",
+        "bedroom_type",
+        "survey_year",
+    }
+
+    # Column name mappings from CMHC export format
+    COLUMN_MAPPINGS = {
+        "Zone Code": "zone_code",
+        "Zone Name": "zone_name",
+        "Bedroom Type": "bedroom_type",
+        "Survey Year": "survey_year",
+        "Universe": "universe",
+        "Average Rent ($)": "avg_rent",
+        "Median Rent ($)": "median_rent",
+        "Vacancy Rate (%)": "vacancy_rate",
+        "Availability Rate (%)": "availability_rate",
+        "Turnover Rate (%)": "turnover_rate",
+        "% Change in Rent": "rent_change_pct",
+        "Reliability Code": "reliability_code",
+    }
+
+    def __init__(self, csv_path: Path) -> None:
+        """Initialize parser with path to CSV file.
+
+        Args:
+            csv_path: Path to the CMHC CSV export file.
+        """
+        self.csv_path = csv_path
+        self._validate_path()
+
+    def _validate_path(self) -> None:
+        """Validate that the CSV path exists and is readable."""
+        if not self.csv_path.exists():
+            raise FileNotFoundError(f"CSV not found: {self.csv_path}")
+        if not self.csv_path.suffix.lower() == ".csv":
+            raise ValueError(f"Expected CSV file, got: {self.csv_path.suffix}")
+
+    def parse(self) -> CMHCAnnualSurvey:
+        """Parse the CSV and return structured data.
+
+        Returns:
+            CMHCAnnualSurvey containing all extracted records.
+
+        Raises:
+            ValueError: If required columns are missing.
+        """
+        df = self._load_csv()
+        df = self._normalize_columns(df)
+        self._validate_columns(df)
+        records = self._convert_to_records(df)
+        survey_year = self._infer_survey_year(df)
+
+        return CMHCAnnualSurvey(survey_year=survey_year, records=records)
+
+    def _load_csv(self) -> pd.DataFrame:
+        """Load CSV file into DataFrame.
+
+        Returns:
+            Raw DataFrame from CSV.
+        """
+        return pd.read_csv(self.csv_path)
+
+    def _normalize_columns(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Normalize column names to standard format.
+
+        Args:
+            df: DataFrame with original column names.
+
+        Returns:
+            DataFrame with normalized column names.
+        """
+        rename_map = {k: v for k, v in self.COLUMN_MAPPINGS.items() if k in df.columns}
+        return df.rename(columns=rename_map)
+
+    def _validate_columns(self, df: pd.DataFrame) -> None:
+        """Validate that all required columns are present.
+
+        Args:
+            df: DataFrame to validate.
+
+        Raises:
+            ValueError: If required columns are missing.
+        """
+        missing = self.REQUIRED_COLUMNS - set(df.columns)
+        if missing:
+            raise ValueError(f"Missing required columns: {missing}")
+
+    def _convert_to_records(self, df: pd.DataFrame) -> list[CMHCRentalRecord]:
+        """Convert DataFrame rows to validated schema records.
+
+        Args:
+            df: Normalized DataFrame.
+
+        Returns:
+            List of validated CMHCRentalRecord objects.
+        """
+        records = []
+        for _, row in df.iterrows():
+            record_data = row.to_dict()
+            # Handle NaN values
+            record_data = {
+                k: (None if pd.isna(v) else v) for k, v in record_data.items()
+            }
+            records.append(CMHCRentalRecord(**cast(dict[str, Any], record_data)))
+        return records
+
+    def _infer_survey_year(self, df: pd.DataFrame) -> int:
+        """Infer survey year from data.
+
+        Args:
+            df: DataFrame with survey_year column.
+
+        Returns:
+            Survey year as integer.
+        """
+        if "survey_year" in df.columns:
+            return int(df["survey_year"].iloc[0])
+        raise ValueError("Cannot infer survey year from data.")

portfolio_app/toronto/parsers/geo.py

@@ -0,0 +1,463 @@
+"""GeoJSON parser for geographic boundary files.
+
+This module provides parsers for loading geographic boundary files
+(GeoJSON format) and converting them to Pydantic schemas for database
+loading or direct use in Plotly choropleth maps.
+"""
+
+import json
+from pathlib import Path
+from typing import Any
+
+from pyproj import Transformer
+from shapely.geometry import mapping, shape
+from shapely.ops import transform
+
+from portfolio_app.toronto.schemas import CMHCZone, Neighbourhood, TRREBDistrict
+from portfolio_app.toronto.schemas.dimensions import AreaType
+
+# Transformer for reprojecting from Web Mercator to WGS84
+_TRANSFORMER_3857_TO_4326 = Transformer.from_crs(
+    "EPSG:3857", "EPSG:4326", always_xy=True
+)
+
+
+def load_geojson(path: Path) -> dict[str, Any]:
+    """Load a GeoJSON file and return as dictionary.
+
+    Args:
+        path: Path to the GeoJSON file.
+
+    Returns:
+        GeoJSON as dictionary (FeatureCollection).
+
+    Raises:
+        FileNotFoundError: If file does not exist.
+        ValueError: If file is not valid GeoJSON.
+    """
+    if not path.exists():
+        raise FileNotFoundError(f"GeoJSON file not found: {path}")
+
+    if path.suffix.lower() not in (".geojson", ".json"):
+        raise ValueError(f"Expected GeoJSON file, got: {path.suffix}")
+
+    with open(path, encoding="utf-8") as f:
+        data = json.load(f)
+
+    if data.get("type") != "FeatureCollection":
+        raise ValueError("GeoJSON must be a FeatureCollection")
+
+    return dict(data)
+
+
+def geometry_to_wkt(geometry: dict[str, Any]) -> str:
+    """Convert GeoJSON geometry to WKT string.
+
+    Args:
+        geometry: GeoJSON geometry dictionary.
+
+    Returns:
+        WKT representation of the geometry.
+    """
+    return str(shape(geometry).wkt)
+
+
+def reproject_geometry(
+    geometry: dict[str, Any], source_crs: str = "EPSG:3857"
+) -> dict[str, Any]:
+    """Reproject a GeoJSON geometry to WGS84 (EPSG:4326).
+
+    Args:
+        geometry: GeoJSON geometry dictionary.
+        source_crs: Source CRS (default EPSG:3857 Web Mercator).
+
+    Returns:
+        GeoJSON geometry in WGS84 coordinates.
+    """
+    if source_crs == "EPSG:3857":
+        transformer = _TRANSFORMER_3857_TO_4326
+    else:
+        transformer = Transformer.from_crs(source_crs, "EPSG:4326", always_xy=True)
+
+    geom = shape(geometry)
+    reprojected = transform(transformer.transform, geom)
+    return dict(mapping(reprojected))
+
+
+class CMHCZoneParser:
+    """Parser for CMHC zone boundary GeoJSON files.
+
+    CMHC zone boundaries are extracted from the R `cmhc` package using
+    `get_cmhc_geography(geography_type="ZONE", cma="Toronto")`.
+
+    Expected GeoJSON properties:
+        - zone_code or Zone_Code: Zone identifier
+        - zone_name or Zone_Name: Zone name
+    """
+
+    # Property name mappings for different GeoJSON formats
+    CODE_PROPERTIES = ["zone_code", "Zone_Code", "ZONE_CODE", "zonecode", "code"]
+    NAME_PROPERTIES = [
+        "zone_name",
+        "Zone_Name",
+        "ZONE_NAME",
+        "ZONE_NAME_EN",
+        "NAME_EN",
+        "zonename",
+        "name",
+        "NAME",
+    ]
+
+    def __init__(self, geojson_path: Path) -> None:
+        """Initialize parser with path to GeoJSON file.
+
+        Args:
+            geojson_path: Path to the CMHC zones GeoJSON file.
+        """
+        self.geojson_path = geojson_path
+        self._geojson: dict[str, Any] | None = None
+
+    @property
+    def geojson(self) -> dict[str, Any]:
+        """Lazy-load and return raw GeoJSON data."""
+        if self._geojson is None:
+            self._geojson = load_geojson(self.geojson_path)
+        return self._geojson
+
+    def _find_property(
+        self, properties: dict[str, Any], candidates: list[str]
+    ) -> str | None:
+        """Find a property value by checking multiple candidate names."""
+        for name in candidates:
+            if name in properties and properties[name] is not None:
+                return str(properties[name])
+        return None
+
+    def parse(self) -> list[CMHCZone]:
+        """Parse GeoJSON and return list of CMHCZone schemas.
+
+        Returns:
+            List of validated CMHCZone objects.
+
+        Raises:
+            ValueError: If required properties are missing.
+        """
+        zones = []
+        for feature in self.geojson.get("features", []):
+            props = feature.get("properties", {})
+            geom = feature.get("geometry")
+
+            zone_code = self._find_property(props, self.CODE_PROPERTIES)
+            zone_name = self._find_property(props, self.NAME_PROPERTIES)
+
+            if not zone_code:
+                raise ValueError(
+                    f"Zone code not found in properties: {list(props.keys())}"
+                )
+            if not zone_name:
+                zone_name = zone_code  # Fallback to code if name missing
+
+            geometry_wkt = geometry_to_wkt(geom) if geom else None
+
+            zones.append(
+                CMHCZone(
+                    zone_code=zone_code,
+                    zone_name=zone_name,
+                    geometry_wkt=geometry_wkt,
+                )
+            )
+
+        return zones
+
+    def _needs_reprojection(self) -> bool:
+        """Check if GeoJSON needs reprojection to WGS84."""
+        crs = self.geojson.get("crs", {})
+        crs_name = crs.get("properties", {}).get("name", "")
+        # EPSG:3857 or Web Mercator needs reprojection
+        return "3857" in crs_name or "900913" in crs_name
+
+    def get_geojson_for_choropleth(
+        self, key_property: str = "zone_code"
+    ) -> dict[str, Any]:
+        """Get GeoJSON formatted for Plotly choropleth maps.
+
+        Ensures the feature properties include a standardized key for
+        joining with data. Automatically reprojects from EPSG:3857 to
+        WGS84 if needed.
+
+        Args:
+            key_property: Property name to use as feature identifier.
+
+        Returns:
+            GeoJSON FeatureCollection with standardized properties in WGS84.
+        """
+        needs_reproject = self._needs_reprojection()
+        features = []
+
+        for feature in self.geojson.get("features", []):
+            props = feature.get("properties", {})
+            new_props = dict(props)
+
+            # Ensure standardized property names exist
+            zone_code = self._find_property(props, self.CODE_PROPERTIES)
+            zone_name = self._find_property(props, self.NAME_PROPERTIES)
+
+            new_props["zone_code"] = zone_code
+            new_props["zone_name"] = zone_name or zone_code
+
+            # Reproject geometry if needed
+            geometry = feature.get("geometry")
+            if needs_reproject and geometry:
+                geometry = reproject_geometry(geometry)
+
+            features.append(
+                {
+                    "type": "Feature",
+                    "properties": new_props,
+                    "geometry": geometry,
+                }
+            )
+
+        return {"type": "FeatureCollection", "features": features}
+
+
+class TRREBDistrictParser:
+    """Parser for TRREB district boundary GeoJSON files.
+
+    TRREB district boundaries are manually digitized from the TRREB PDF map
+    using QGIS.
+
+    Expected GeoJSON properties:
+        - district_code: District code (W01, C01, E01, etc.)
+        - district_name: District name
+        - area_type: West, Central, East, or North
+    """
+
+    CODE_PROPERTIES = [
+        "district_code",
+        "District_Code",
+        "DISTRICT_CODE",
+        "districtcode",
+        "code",
+    ]
+    NAME_PROPERTIES = [
+        "district_name",
+        "District_Name",
+        "DISTRICT_NAME",
+        "districtname",
+        "name",
+        "NAME",
+    ]
+    AREA_PROPERTIES = [
+        "area_type",
+        "Area_Type",
+        "AREA_TYPE",
+        "areatype",
+        "area",
+        "type",
+    ]
+
+    def __init__(self, geojson_path: Path) -> None:
+        """Initialize parser with path to GeoJSON file."""
+        self.geojson_path = geojson_path
+        self._geojson: dict[str, Any] | None = None
+
+    @property
+    def geojson(self) -> dict[str, Any]:
+        """Lazy-load and return raw GeoJSON data."""
+        if self._geojson is None:
+            self._geojson = load_geojson(self.geojson_path)
+        return self._geojson
+
+    def _find_property(
+        self, properties: dict[str, Any], candidates: list[str]
+    ) -> str | None:
+        """Find a property value by checking multiple candidate names."""
+        for name in candidates:
+            if name in properties and properties[name] is not None:
+                return str(properties[name])
+        return None
+
+    def _infer_area_type(self, district_code: str) -> AreaType:
+        """Infer area type from district code prefix."""
+        prefix = district_code[0].upper()
+        mapping = {"W": AreaType.WEST, "C": AreaType.CENTRAL, "E": AreaType.EAST}
+        return mapping.get(prefix, AreaType.NORTH)
+
+    def parse(self) -> list[TRREBDistrict]:
+        """Parse GeoJSON and return list of TRREBDistrict schemas."""
+        districts = []
+        for feature in self.geojson.get("features", []):
+            props = feature.get("properties", {})
+            geom = feature.get("geometry")
+
+            district_code = self._find_property(props, self.CODE_PROPERTIES)
+            district_name = self._find_property(props, self.NAME_PROPERTIES)
+            area_type_str = self._find_property(props, self.AREA_PROPERTIES)
+
+            if not district_code:
+                raise ValueError(
+                    f"District code not found in properties: {list(props.keys())}"
+                )
+            if not district_name:
+                district_name = district_code
+
+            # Infer or parse area type
+            if area_type_str:
+                try:
+                    area_type = AreaType(area_type_str)
+                except ValueError:
+                    area_type = self._infer_area_type(district_code)
+            else:
+                area_type = self._infer_area_type(district_code)
+
+            geometry_wkt = geometry_to_wkt(geom) if geom else None
+
+            districts.append(
+                TRREBDistrict(
+                    district_code=district_code,
+                    district_name=district_name,
+                    area_type=area_type,
+                    geometry_wkt=geometry_wkt,
+                )
+            )
+
+        return districts
+
+    def get_geojson_for_choropleth(
+        self, key_property: str = "district_code"
+    ) -> dict[str, Any]:
+        """Get GeoJSON formatted for Plotly choropleth maps."""
+        features = []
+        for feature in self.geojson.get("features", []):
+            props = feature.get("properties", {})
+            new_props = dict(props)
+
+            district_code = self._find_property(props, self.CODE_PROPERTIES)
+            district_name = self._find_property(props, self.NAME_PROPERTIES)
+
+            new_props["district_code"] = district_code
+            new_props["district_name"] = district_name or district_code
+
+            features.append(
+                {
+                    "type": "Feature",
+                    "properties": new_props,
+                    "geometry": feature.get("geometry"),
+                }
+            )
+
+        return {"type": "FeatureCollection", "features": features}
+
+
+class NeighbourhoodParser:
+    """Parser for City of Toronto neighbourhood boundary GeoJSON files.
+
+    Neighbourhood boundaries are from the City of Toronto Open Data portal.
+
+    Expected GeoJSON properties:
+        - neighbourhood_id or AREA_ID: Neighbourhood ID (1-158)
+        - name or AREA_NAME: Neighbourhood name
+    """
+
+    ID_PROPERTIES = [
+        "neighbourhood_id",
+        "AREA_SHORT_CODE",  # City of Toronto 158 neighbourhoods
+        "AREA_LONG_CODE",
+        "AREA_ID",
+        "area_id",
+        "id",
+        "ID",
+        "HOOD_ID",
+    ]
+    NAME_PROPERTIES = [
+        "AREA_NAME",  # City of Toronto 158 neighbourhoods
+        "name",
+        "NAME",
+        "area_name",
+        "neighbourhood_name",
+    ]
+
+    def __init__(self, geojson_path: Path) -> None:
+        """Initialize parser with path to GeoJSON file."""
+        self.geojson_path = geojson_path
+        self._geojson: dict[str, Any] | None = None
+
+    @property
+    def geojson(self) -> dict[str, Any]:
+        """Lazy-load and return raw GeoJSON data."""
+        if self._geojson is None:
+            self._geojson = load_geojson(self.geojson_path)
+        return self._geojson
+
+    def _find_property(
+        self, properties: dict[str, Any], candidates: list[str]
+    ) -> str | None:
+        """Find a property value by checking multiple candidate names."""
+        for name in candidates:
+            if name in properties and properties[name] is not None:
+                return str(properties[name])
+        return None
+
+    def parse(self) -> list[Neighbourhood]:
+        """Parse GeoJSON and return list of Neighbourhood schemas.
+
+        Note: This parser only extracts ID, name, and geometry.
+        Census enrichment data (population, income, etc.) should be
+        loaded separately and merged.
+        """
+        neighbourhoods = []
+        for feature in self.geojson.get("features", []):
+            props = feature.get("properties", {})
+            geom = feature.get("geometry")
+
+            neighbourhood_id_str = self._find_property(props, self.ID_PROPERTIES)
+            name = self._find_property(props, self.NAME_PROPERTIES)
+
+            if not neighbourhood_id_str:
+                raise ValueError(
+                    f"Neighbourhood ID not found in properties: {list(props.keys())}"
+                )
+
+            neighbourhood_id = int(neighbourhood_id_str)
+            if not name:
+                name = f"Neighbourhood {neighbourhood_id}"
+
+            geometry_wkt = geometry_to_wkt(geom) if geom else None
+
+            neighbourhoods.append(
+                Neighbourhood(
+                    neighbourhood_id=neighbourhood_id,
+                    name=name,
+                    geometry_wkt=geometry_wkt,
+                )
+            )
+
+        return neighbourhoods
+
+    def get_geojson_for_choropleth(
+        self, key_property: str = "neighbourhood_id"
+    ) -> dict[str, Any]:
+        """Get GeoJSON formatted for Plotly choropleth maps."""
+        features = []
+        for feature in self.geojson.get("features", []):
+            props = feature.get("properties", {})
+            new_props = dict(props)
+
+            neighbourhood_id = self._find_property(props, self.ID_PROPERTIES)
+            name = self._find_property(props, self.NAME_PROPERTIES)
+
+            new_props["neighbourhood_id"] = (
+                int(neighbourhood_id) if neighbourhood_id else None
+            )
+            new_props["name"] = name
+
+            features.append(
+                {
+                    "type": "Feature",
+                    "properties": new_props,
+                    "geometry": feature.get("geometry"),
+                }
+            )
+
+        return {"type": "FeatureCollection", "features": features}

portfolio_app/toronto/parsers/trreb.py

@@ -0,0 +1,82 @@
+"""TRREB PDF parser for monthly market watch reports.
+
+This module provides the structure for parsing TRREB (Toronto Regional Real Estate Board)
+monthly Market Watch PDF reports into structured data.
+"""
+
+from pathlib import Path
+from typing import Any
+
+from portfolio_app.toronto.schemas import TRREBMonthlyRecord, TRREBMonthlyReport
+
+
+class TRREBParser:
+    """Parser for TRREB Market Watch PDF reports.
+
+    TRREB publishes monthly Market Watch reports as PDFs containing:
+    - Summary statistics by area (416, 905, Total)
+    - District-level breakdowns
+    - Year-over-year comparisons
+
+    The parser extracts tabular data from these PDFs and validates
+    against the TRREBMonthlyRecord schema.
+    """
+
+    def __init__(self, pdf_path: Path) -> None:
+        """Initialize parser with path to PDF file.
+
+        Args:
+            pdf_path: Path to the TRREB Market Watch PDF file.
+        """
+        self.pdf_path = pdf_path
+        self._validate_path()
+
+    def _validate_path(self) -> None:
+        """Validate that the PDF path exists and is readable."""
+        if not self.pdf_path.exists():
+            raise FileNotFoundError(f"PDF not found: {self.pdf_path}")
+        if not self.pdf_path.suffix.lower() == ".pdf":
+            raise ValueError(f"Expected PDF file, got: {self.pdf_path.suffix}")
+
+    def parse(self) -> TRREBMonthlyReport:
+        """Parse the PDF and return structured data.
+
+        Returns:
+            TRREBMonthlyReport containing all extracted records.
+
+        Raises:
+            NotImplementedError: PDF parsing not yet implemented.
+        """
+        raise NotImplementedError(
+            "PDF parsing requires pdfplumber/tabula-py. "
+            "Implementation pending Sprint 4 data ingestion."
+        )
+
+    def _extract_tables(self) -> list[dict[str, Any]]:
+        """Extract raw tables from PDF pages.
+
+        Returns:
+            List of dictionaries representing table data.
+        """
+        raise NotImplementedError("Table extraction not yet implemented.")
+
+    def _parse_district_table(
+        self, table_data: list[dict[str, Any]]
+    ) -> list[TRREBMonthlyRecord]:
+        """Parse district-level statistics table.
+
+        Args:
+            table_data: Raw table data extracted from PDF.
+
+        Returns:
+            List of validated TRREBMonthlyRecord objects.
+        """
+        raise NotImplementedError("District table parsing not yet implemented.")
+
+    def _infer_report_date(self) -> tuple[int, int]:
+        """Infer report year and month from PDF filename or content.
+
+        Returns:
+            Tuple of (year, month).
+        """
+        raise NotImplementedError("Date inference not yet implemented.")

portfolio_app/toronto/schemas/__init__.py

@@ -1 +1,39 @@
 """Pydantic schemas for Toronto housing data validation."""
+
+from .cmhc import BedroomType, CMHCAnnualSurvey, CMHCRentalRecord, ReliabilityCode
+from .dimensions import (
+    AreaType,
+    CMHCZone,
+    Confidence,
+    ExpectedDirection,
+    Neighbourhood,
+    PolicyCategory,
+    PolicyEvent,
+    PolicyLevel,
+    TimeDimension,
+    TRREBDistrict,
+)
+from .trreb import TRREBMonthlyRecord, TRREBMonthlyReport
+
+__all__ = [
+    # TRREB
+    "TRREBMonthlyRecord",
+    "TRREBMonthlyReport",
+    # CMHC
+    "CMHCRentalRecord",
+    "CMHCAnnualSurvey",
+    "BedroomType",
+    "ReliabilityCode",
+    # Dimensions
+    "TimeDimension",
+    "TRREBDistrict",
+    "CMHCZone",
+    "Neighbourhood",
+    "PolicyEvent",
+    # Enums
+    "AreaType",
+    "PolicyLevel",
+    "PolicyCategory",
+    "ExpectedDirection",
+    "Confidence",
+]

portfolio_app/toronto/schemas/cmhc.py

@@ -0,0 +1,81 @@
+"""Pydantic schemas for CMHC rental market data."""
+
+from decimal import Decimal
+from enum import Enum
+
+from pydantic import BaseModel, Field
+
+
+class BedroomType(str, Enum):
+    """CMHC bedroom type categories."""
+
+    BACHELOR = "Bachelor"
+    ONE_BED = "1 Bedroom"
+    TWO_BED = "2 Bedroom"
+    THREE_BED_PLUS = "3 Bedroom+"
+    TOTAL = "Total"
+
+
+class ReliabilityCode(str, Enum):
+    """CMHC data reliability codes.
+
+    Based on coefficient of variation (CV).
+    """
+
+    EXCELLENT = "a"  # CV <= 2.5%
+    GOOD = "b"  # 2.5% < CV <= 5%
+    FAIR = "c"  # 5% < CV <= 10%
+    POOR = "d"  # CV > 10%
+    SUPPRESSED = "**"  # Sample too small
+
+
+class CMHCRentalRecord(BaseModel):
+    """Schema for a single CMHC rental survey record.
+
+    Represents rental data for one zone and bedroom type in one survey year.
+    """
+
+    survey_year: int = Field(ge=1990, description="Survey year (October snapshot)")
+    zone_code: str = Field(max_length=10, description="CMHC zone identifier")
+    zone_name: str = Field(max_length=100, description="Zone name")
+    bedroom_type: BedroomType = Field(description="Bedroom category")
+    universe: int | None = Field(
+        default=None, ge=0, description="Total rental units in zone"
+    )
+    vacancy_rate: Decimal | None = Field(
+        default=None, ge=0, le=100, description="Vacancy rate (%)"
+    )
+    vacancy_rate_reliability: ReliabilityCode | None = Field(default=None)
+    availability_rate: Decimal | None = Field(
+        default=None, ge=0, le=100, description="Availability rate (%)"
+    )
+    average_rent: Decimal | None = Field(
+        default=None, ge=0, description="Average monthly rent ($)"
+    )
+    average_rent_reliability: ReliabilityCode | None = Field(default=None)
+    median_rent: Decimal | None = Field(
+        default=None, ge=0, description="Median monthly rent ($)"
+    )
+    rent_change_pct: Decimal | None = Field(
+        default=None, description="YoY rent change (%)"
+    )
+    turnover_rate: Decimal | None = Field(
+        default=None, ge=0, le=100, description="Unit turnover rate (%)"
+    )
+
+    model_config = {"str_strip_whitespace": True}
+
+
+class CMHCAnnualSurvey(BaseModel):
+    """Schema for a complete CMHC annual survey for Toronto.
+
+    Contains all zone and bedroom type combinations for one survey year.
+    """
+
+    survey_year: int
+    records: list[CMHCRentalRecord]
+
+    @property
+    def zone_count(self) -> int:
+        """Number of unique zones in survey."""
+        return len({r.zone_code for r in self.records})

portfolio_app/toronto/schemas/dimensions.py

@@ -0,0 +1,121 @@
+"""Pydantic schemas for dimension tables."""
+
+from datetime import date
+from decimal import Decimal
+from enum import Enum
+
+from pydantic import BaseModel, Field, HttpUrl
+
+
+class PolicyLevel(str, Enum):
+    """Government level for policy events."""
+
+    FEDERAL = "federal"
+    PROVINCIAL = "provincial"
+    MUNICIPAL = "municipal"
+
+
+class PolicyCategory(str, Enum):
+    """Policy event category."""
+
+    MONETARY = "monetary"
+    TAX = "tax"
+    REGULATORY = "regulatory"
+    SUPPLY = "supply"
+    ECONOMIC = "economic"
+
+
+class ExpectedDirection(str, Enum):
+    """Expected price impact direction."""
+
+    BULLISH = "bullish"  # Expected to increase prices
+    BEARISH = "bearish"  # Expected to decrease prices
+    NEUTRAL = "neutral"  # Uncertain or mixed impact
+
+
+class Confidence(str, Enum):
+    """Confidence level in policy event data."""
+
+    HIGH = "high"
+    MEDIUM = "medium"
+    LOW = "low"
+
+
+class AreaType(str, Enum):
+    """TRREB area type."""
+
+    WEST = "West"
+    CENTRAL = "Central"
+    EAST = "East"
+    NORTH = "North"
+
+
+class TimeDimension(BaseModel):
+    """Schema for time dimension record."""
+
+    date_key: int = Field(description="Date key in YYYYMMDD format")
+    full_date: date
+    year: int = Field(ge=2000, le=2100)
+    month: int = Field(ge=1, le=12)
+    quarter: int = Field(ge=1, le=4)
+    month_name: str = Field(max_length=20)
+    is_month_start: bool = True
+
+
+class TRREBDistrict(BaseModel):
+    """Schema for TRREB district dimension."""
+
+    district_code: str = Field(max_length=3, description="W01, C01, E01, etc.")
+    district_name: str = Field(max_length=100)
+    area_type: AreaType
+    geometry_wkt: str | None = Field(default=None, description="WKT geometry string")
+
+
+class CMHCZone(BaseModel):
+    """Schema for CMHC zone dimension."""
+
+    zone_code: str = Field(max_length=10)
+    zone_name: str = Field(max_length=100)
+    geometry_wkt: str | None = Field(default=None, description="WKT geometry string")
+
+
+class Neighbourhood(BaseModel):
+    """Schema for City of Toronto neighbourhood dimension.
+
+    Note: No FK to fact tables in V1 - reference overlay only.
+    """
+
+    neighbourhood_id: int = Field(ge=1, le=200)
+    name: str = Field(max_length=100)
+    geometry_wkt: str | None = Field(default=None)
+    population: int | None = Field(default=None, ge=0)
+    land_area_sqkm: Decimal | None = Field(default=None, ge=0)
+    pop_density_per_sqkm: Decimal | None = Field(default=None, ge=0)
+    pct_bachelors_or_higher: Decimal | None = Field(default=None, ge=0, le=100)
+    median_household_income: Decimal | None = Field(default=None, ge=0)
+    pct_owner_occupied: Decimal | None = Field(default=None, ge=0, le=100)
+    pct_renter_occupied: Decimal | None = Field(default=None, ge=0, le=100)
+    census_year: int = Field(default=2021, description="Census year for SCD tracking")
+
+
+class PolicyEvent(BaseModel):
+    """Schema for policy event dimension.
+
+    Used for time-series annotation. No causation claims.
+    """
+
+    event_date: date = Field(description="Date event was announced/occurred")
+    effective_date: date | None = Field(
+        default=None, description="Date policy took effect"
+    )
+    level: PolicyLevel
+    category: PolicyCategory
+    title: str = Field(max_length=200, description="Short event title for display")
+    description: str | None = Field(
+        default=None, description="Longer description for tooltip"
+    )
+    expected_direction: ExpectedDirection
+    source_url: HttpUrl | None = Field(default=None)
+    confidence: Confidence = Field(default=Confidence.MEDIUM)
+
+    model_config = {"str_strip_whitespace": True}

portfolio_app/toronto/schemas/trreb.py

@@ -0,0 +1,52 @@
+"""Pydantic schemas for TRREB monthly market data."""
+
+from datetime import date
+from decimal import Decimal
+
+from pydantic import BaseModel, Field
+
+
+class TRREBMonthlyRecord(BaseModel):
+    """Schema for a single TRREB monthly summary record.
+
+    Represents aggregated sales data for one district in one month.
+    """
+
+    report_date: date = Field(description="First of month (YYYY-MM-01)")
+    area_code: str = Field(
+        max_length=3, description="District code (W01, C01, E01, etc.)"
+    )
+    area_name: str = Field(max_length=100, description="District name")
+    area_type: str = Field(max_length=10, description="West / Central / East / North")
+    sales: int = Field(ge=0, description="Number of transactions")
+    dollar_volume: Decimal = Field(ge=0, description="Total sales volume ($)")
+    avg_price: Decimal = Field(ge=0, description="Average sale price ($)")
+    median_price: Decimal = Field(ge=0, description="Median sale price ($)")
+    new_listings: int = Field(ge=0, description="New listings count")
+    active_listings: int = Field(ge=0, description="Active listings at month end")
+    avg_sp_lp: Decimal = Field(
+        ge=0, le=200, description="Avg sale price / list price ratio (%)"
+    )
+    avg_dom: int = Field(ge=0, description="Average days on market")
+
+    model_config = {"str_strip_whitespace": True}
+
+
+class TRREBMonthlyReport(BaseModel):
+    """Schema for a complete TRREB monthly report.
+
+    Contains all district records for a single month.
+    """
+
+    report_date: date
+    records: list[TRREBMonthlyRecord]
+
+    @property
+    def total_sales(self) -> int:
+        """Total sales across all districts."""
+        return sum(r.sales for r in self.records)
+
+    @property
+    def district_count(self) -> int:
+        """Number of districts in report."""
+        return len(self.records)

pyproject.toml

@@ -39,6 +39,7 @@ dependencies = [
     "dash>=3.3",
     "plotly>=6.5",
     "dash-mantine-components>=2.4",
+    "dash-iconify>=0.1",
 
     # PDF Parsing
     "pdfplumber>=0.11",
@@ -132,17 +133,20 @@ skip-magic-trailing-comma = false
 python_version = "3.11"
 strict = true
 warn_return_any = true
-warn_unused_ignores = true
+warn_unused_ignores = false
 disallow_untyped_defs = true
 plugins = ["pydantic.mypy"]
 
 [[tool.mypy.overrides]]
 module = [
     "dash.*",
+    "dash_mantine_components.*",
+    "dash_iconify.*",
     "plotly.*",
     "geopandas.*",
     "shapely.*",
     "pdfplumber.*",
     "tabula.*",
+    "pydantic_settings.*",
 ]
 ignore_missing_imports = true

scripts/db/init_schema.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+"""Initialize database schema.
+
+Usage:
+    python scripts/db/init_schema.py
+
+This script creates all SQLAlchemy tables in the database.
+Run this after docker-compose up to initialize the schema.
+"""
+
+import sys
+from pathlib import Path
+
+# Add project root to path
+sys.path.insert(0, str(Path(__file__).parent.parent.parent))
+
+from portfolio_app.toronto.models import create_tables, get_engine  # noqa: E402
+
+
+def main() -> int:
+    """Initialize the database schema."""
+    print("Initializing database schema...")
+
+    try:
+        engine = get_engine()
+
+        # Test connection
+        with engine.connect() as conn:
+            result = conn.execute("SELECT 1")
+            result.fetchone()
+        print("Database connection successful")
+
+        # Create all tables
+        create_tables()
+        print("Schema created successfully")
+
+        # List created tables
+        from sqlalchemy import inspect
+
+        inspector = inspect(engine)
+        tables = inspector.get_table_names()
+        print(f"Created tables: {', '.join(tables)}")
+
+        return 0
+
+    except Exception as e:
+        print(f"Error: {e}", file=sys.stderr)
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

tests/test_placeholder.py

@@ -0,0 +1,6 @@
+"""Placeholder test to ensure pytest collection succeeds."""
+
+
+def test_placeholder():
+    """Remove this once real tests are added."""
+    assert True