personal-portfolio/docs/toronto_housing_dashboard_spec_v5.md

Toronto Housing Price Dashboard

Portfolio Project — Data Specification & Architecture

Version: 5.1 Last Updated: January 2026 Status: Specification Complete

---

Document Context

Attribute	Value
Parent Document	portfolio_project_plan_v5.md
Role	Detailed specification for Toronto Housing Dashboard
Scope	Data schemas, source URLs, geographic boundaries, V1/V2 decisions

Rule: For overall project scope, phasing, tech stack, and deployment architecture, see portfolio_project_plan_v5.md. This document provides implementation-level detail for the Toronto Housing project specifically.

Terminology Note: This document uses Stages 1–4 to describe Toronto Housing implementation steps. These are distinct from the Phases 1–5 in portfolio_project_plan_v5.md, which describe the overall portfolio project lifecycle.

---

Project Overview

A dashboard analyzing housing price variations across Toronto neighbourhoods over time, with dual analysis tracks:

Track	Data Domain	Primary Source	Geographic Unit
Purchases	Sales transactions	TRREB Monthly Reports	~35 Districts
Rentals	Rental market stats	CMHC Rental Market Survey	~20 Zones

Core Visualization: Interactive choropleth map of Toronto with toggle between rental/purchase analysis, time-series exploration by month/year.

Enrichment Layer (V1: overlay only): Neighbourhood-level demographic and socioeconomic context including population density, education attainment, and income. Crime data deferred to Phase 4 of the portfolio project (post-Energy project).

Tech Stack & Deployment: See portfolio_project_plan_v5.md → Tech Stack, Deployment Architecture

---

Geographic Layers

Layer Architecture

┌─────────────────────────────────────────────────────────────────┐
│ City of Toronto Official Neighbourhoods (158) │ ← Reference overlay + Enrichment data
├─────────────────────────────────────────────────────────────────┤
│ TRREB Districts (~35) — W01, C01, E01, etc. │ ← Purchase data
├─────────────────────────────────────────────────────────────────┤
│ CMHC Survey Zones (~20) — Census Tract aligned │ ← Rental data
└─────────────────────────────────────────────────────────────────┘

Boundary Files

Layer	Zones	Format	Source	Status
City Neighbourhoods	158	GeoJSON, Shapefile	GitHub - jasonicarter/toronto-geojson	✅ Ready to use
TRREB Districts	~35	PDF only	TRREB Toronto Map PDF	⚠ Requires manual digitization
CMHC Zones	~20	R package	R cmhc package via get_cmhc_geography()	✅ Available (see note)

Digitization Task: TRREB Districts

Input: TRREB Toronto PDF map Output: GeoJSON with district codes (W01-W10, C01-C15, E01-E11) Tool: QGIS

Process:

1. Import PDF as raster layer in QGIS
2. Create vector layer with polygon features
3. Trace district boundaries
4. Add attributes: district_code, district_name, area_type (West/Central/East)
5. Export as GeoJSON (WGS84 / EPSG:4326)

CMHC Zone Boundaries

Source: The R cmhc package provides CMHC survey geography via the get_cmhc_geography() function.

Extraction Process:

# In R
library(cmhc)
library(sf)

# Get Toronto CMA zones
toronto_zones <- get_cmhc_geography(
geography_type = "ZONE",
cma = "Toronto"
)

# Export to GeoJSON for Python/PostGIS
st_write(toronto_zones, "cmhc_zones.geojson", driver = "GeoJSON")

Output: data/toronto/raw/geo/cmhc_zones.geojson

Why R?: CMHC zone boundaries are not published as standalone files. The cmhc R package is the only reliable programmatic source. One-time extraction, then use GeoJSON in Python stack.

⚠ Neighbourhood Boundary Change (140 → 158)

The City of Toronto updated from 140 to 158 social planning neighbourhoods in April 2021. This affects data alignment:

Data Source	Pre-2021	Post-2021	Handling
Census (2016 and earlier)	140 neighbourhoods	N/A	Use 140-model files
Census (2021+)	N/A	158 neighbourhoods	Use 158-model files

V1 Strategy: Use 2021 Census on 158 boundaries only. Defer historical trend analysis to portfolio Phase 4.

---

Data Source #1: TRREB Monthly Market Reports

Source Details

Attribute	Value
Provider	Toronto Regional Real Estate Board
URL	TRREB Market Watch
Format	PDF (monthly reports)
Update Frequency	Monthly
Historical Availability	2007–Present
Access	Public (aggregate data in PDFs)
Extraction Method	PDF parsing (pdfplumber or camelot-py)

Available Tables

Table: trreb_monthly_summary

Location in PDF: Pages 3-4 (Summary by Area)

Column	Data Type	Description
report_date	DATE	First of month (YYYY-MM-01)
area_code	VARCHAR(3)	District code (W01, C01, E01, etc.)
area_name	VARCHAR(100)	District name
area_type	VARCHAR(10)	West / Central / East / North
sales	INTEGER	Number of transactions
dollar_volume	DECIMAL	Total sales volume ($)
avg_price	DECIMAL	Average sale price ($)
median_price	DECIMAL	Median sale price ($)
new_listings	INTEGER	New listings count
active_listings	INTEGER	Active listings at month end
avg_sp_lp	DECIMAL	Avg sale price / list price ratio (%)
avg_dom	INTEGER	Average days on market

Dimensions

Dimension	Granularity	Values
Time	Monthly	2007-01 to present
Geography	District	~35 TRREB districts
Property Type	Aggregate	All residential (no breakdown in summary)

Metrics Available

Metric	Aggregation	Use Case
avg_price	Pre-calculated monthly avg	Primary price indicator
median_price	Pre-calculated monthly median	Robust price indicator
sales	Count	Market activity volume
avg_dom	Average	Market velocity
avg_sp_lp	Ratio	Buyer/seller market indicator
new_listings	Count	Supply indicator
active_listings	Snapshot	Inventory level

⚠ Limitations

• No transaction-level data (aggregates only)
• Property type breakdown requires parsing additional tables
• PDF structure may vary slightly across years
• District boundaries haven't changed since 2011

---

Data Source #2: CMHC Rental Market Survey

Source Details

Attribute	Value
Provider	Canada Mortgage and Housing Corporation
URL	CMHC Housing Market Information Portal
Format	CSV export, API
Update Frequency	Annual (October survey)
Historical Availability	1990–Present
Access	Public, free registration for bulk downloads
Geographic Levels	CMA → Zone → Neighbourhood → Census Tract

Available Tables

Table: cmhc_rental_summary

Portal Path: Toronto → Primary Rental Market → Summary Statistics

Column	Data Type	Description
survey_year	INTEGER	Survey year (October)
zone_code	VARCHAR(10)	CMHC zone identifier
zone_name	VARCHAR(100)	Zone name
bedroom_type	VARCHAR(20)	Bachelor / 1-Bed / 2-Bed / 3-Bed+ / Total
universe	INTEGER	Total rental units in zone
vacancy_rate	DECIMAL	Vacancy rate (%)
vacancy_rate_reliability	VARCHAR(1)	Reliability code (a/b/c/d)
availability_rate	DECIMAL	Availability rate (%)
average_rent	DECIMAL	Average monthly rent ($)
average_rent_reliability	VARCHAR(1)	Reliability code
median_rent	DECIMAL	Median monthly rent ($)
rent_change_pct	DECIMAL	YoY rent change (%)
turnover_rate	DECIMAL	Unit turnover rate (%)

Dimensions

Dimension	Granularity	Values
Time	Annual	1990 to present (October snapshot)
Geography	Zone	~20 CMHC zones in Toronto CMA
Bedroom Type	Category	Bachelor, 1-Bed, 2-Bed, 3-Bed+, Total
Structure Type	Category	Row, Apartment (available in detailed tables)

Metrics Available

Metric	Aggregation	Use Case
average_rent	Pre-calculated avg	Primary rent indicator
median_rent	Pre-calculated median	Robust rent indicator
vacancy_rate	Percentage	Market tightness
availability_rate	Percentage	Supply accessibility
turnover_rate	Percentage	Tenant mobility
rent_change_pct	YoY %	Rent growth tracking
universe	Count	Market size

Reliability Codes

Code	Meaning	Coefficient of Variation
a	Excellent	CV ≤ 2.5%
b	Good	2.5% < CV ≤ 5%
c	Fair	5% < CV ≤ 10%
d	Poor (use with caution)	CV > 10%
**	Data suppressed	Sample too small

⚠ Limitations

• Annual only (no monthly granularity)
• October snapshot (point-in-time)
• Zones are larger than TRREB districts
• Purpose-built rental only (excludes condo rentals in base survey)

---

Data Source #3: City of Toronto Open Data

Source Details

Attribute	Value
Provider	City of Toronto
URL	Toronto Open Data Portal
Format	GeoJSON, Shapefile, CSV
Use Case	Reference layer, demographic enrichment

Relevant Datasets

Dataset: neighbourhoods

Column	Data Type	Description
area_id	INTEGER	Neighbourhood ID (1-158)
area_name	VARCHAR(100)	Official neighbourhood name
geometry	POLYGON	Boundary geometry

Dataset: neighbourhood_profiles (Census-linked)

Column	Data Type	Description
neighbourhood_id	INTEGER	Links to neighbourhoods
population	INTEGER	Total population
avg_household_income	DECIMAL	Average household income
dwelling_count	INTEGER	Total dwellings
owner_pct	DECIMAL	% owner-occupied
renter_pct	DECIMAL	% renter-occupied

Enrichment Potential

Can overlay demographic context on housing data:

• Income brackets by neighbourhood
• Ownership vs rental ratios
• Population density
• Dwelling type distribution

---

Data Source #4: Enrichment Data (Density, Education)

Purpose

Provide socioeconomic context to housing price analysis. Enables questions like:

• Do neighbourhoods with higher education attainment have higher prices?
• How does population density correlate with price per square foot?

Geographic Alignment Reality

Critical constraint: Enrichment data is available at the 158-neighbourhood level, while core housing data sits at TRREB districts (~35) and CMHC zones (~20). These do not align cleanly.

158 Neighbourhoods (fine) → Enrichment data lives here
(no clean crosswalk)
~35 TRREB Districts (coarse) → Purchase data lives here
~20 CMHC Zones (coarse) → Rental data lives here

Available Enrichment Datasets

Dataset: Neighbourhood Profiles (Census)

Attribute	Value
Provider	City of Toronto (via Statistics Canada Census)
URL	Toronto Open Data - Neighbourhood Profiles
Format	CSV, JSON, XML, XLSX
Update Frequency	Every 5 years (Census cycle)
Available Years	2001, 2006, 2011, 2016, 2021
Geographic Unit	158 neighbourhoods (140 pre-2021)

Key Variables:

Variable	Description	Use Case
population	Total population	Density calculation
land_area_sqkm	Area in square kilometers	Density calculation
pop_density_per_sqkm	Population per km	Density metric
pct_bachelors_or_higher	% age 25-64 with bachelor's+	Education proxy
median_household_income	Median total household income	Income metric
avg_household_income	Average total household income	Income metric
pct_owner_occupied	% owner-occupied dwellings	Tenure split
pct_renter_occupied	% renter-occupied dwellings	Tenure split

Download URL (2021, 158 neighbourhoods):

https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/6e19a90f-971c-46b3-852c-0c48c436d1fc/resource/19d4a806-7385-4889-acf2-256f1e079060/download/nbhd_2021_census_profile_full_158model.xlsx

Crime Data — Deferred to Portfolio Phase 4

Crime data (TPS Neighbourhood Crime Rates) is not included in V1 scope. It will be added in portfolio Phase 4 after the Energy Pricing project is complete.

Rationale:

• Crime data is socially/politically sensitive and requires careful methodology documentation
• V1 focuses on core housing metrics and policy events
• Deferral reduces scope creep risk

Future Reference (Portfolio Phase 4):

• Source: TPS Public Safety Data Portal
• Dataset: Neighbourhood Crime Rates (Major Crime Indicators)
• Geographic Unit: 158 neighbourhoods

V1 Enrichment Data Summary

Measure	Source	Geography	Frequency	Format	Status
Population Density	Neighbourhood Profiles	158 neighbourhoods	Census (5-year)	CSV/JSON	✅ Ready
Education Attainment	Neighbourhood Profiles	158 neighbourhoods	Census (5-year)	CSV/JSON	✅ Ready
Median Income	Neighbourhood Profiles	158 neighbourhoods	Census (5-year)	CSV/JSON	✅ Ready
Crime Rates (MCI)	TPS Data Portal	158 neighbourhoods	Annual	GeoJSON/CSV	Deferred to Portfolio Phase 4

---

Data Source #5: Policy Events

Purpose

Provide temporal context for housing price movements. Display as annotation markers on time series charts. No causation claims — correlation/context only.

Event Schema

Table: dim_policy_event

Column	Data Type	Description
event_id	INTEGER (PK)	Auto-increment primary key
event_date	DATE	Date event was announced/occurred
effective_date	DATE	Date policy took effect (if different)
level	VARCHAR(20)	federal / provincial / municipal
category	VARCHAR(20)	monetary / tax / regulatory / supply / economic
title	VARCHAR(200)	Short event title for display
description	TEXT	Longer description for tooltip
expected_direction	VARCHAR(10)	bearish / bullish / neutral
source_url	VARCHAR(500)	Link to official announcement/documentation
confidence	VARCHAR(10)	high / medium / low
created_at	TIMESTAMP	Record creation timestamp

Event Tiers

Tier	Level	Category Examples	Inclusion Criteria
1	Federal	BoC rate decisions, OSFI stress tests	Always include; objective, documented
1	Provincial	Fair Housing Plan, foreign buyer tax, rent control	Always include; legislative record
2	Municipal	Zoning reforms, development charges	Include if material impact expected
2	Economic	COVID measures, major employer closures	Include if Toronto-specific impact
3	Market	Major project announcements	Strict criteria; must be verifiable

Expected Direction Values

Value	Meaning	Example
bullish	Expected to increase prices	Rate cut, supply restriction
bearish	Expected to decrease prices	Rate hike, foreign buyer tax
neutral	Uncertain or mixed impact	Regulatory clarification

⚠ Caveats

• No causation claims: Events are context, not explanation
• Lag effects: Policy impact may not be immediate
• Confounding factors: Multiple simultaneous influences
• Display only: No statistical analysis in V1

Sample Events (Tier 1)

Date	Level	Category	Title	Direction
2017-04-20	provincial	tax	Ontario Fair Housing Plan	bearish
2018-01-01	federal	regulatory	OSFI B-20 Stress Test	bearish
2020-03-27	federal	monetary	BoC Emergency Rate Cut (0.25%)	bullish
2022-03-02	federal	monetary	BoC Rate Hike Cycle Begins	bearish
2023-06-01	federal	tax	Federal 2-Year Foreign Buyer Ban	bearish

---

Data Integration Strategy

Temporal Alignment

Source	Native Frequency	Alignment Strategy
TRREB	Monthly	Use as-is
CMHC	Annual (October)	Spread to monthly OR display annual overlay
Census/Enrichment	5-year	Static snapshot; display as reference
Policy Events	Event-based	Display as vertical markers on time axis

Recommendation: Keep separate time axes. TRREB monthly for purchases, CMHC annual for rentals. Don't force artificial monthly rental data.

Geographic Alignment

┌─────────────────────────────────────────────────────────────────┐
│ VISUALIZATION APPROACH │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Purchase Mode Rental Mode │
│ ───────────────── ────────────── │
│ Map: TRREB Districts Map: CMHC Zones │
│ Time: Monthly slider Time: Annual selector │
│ Metrics: Price, Sales Metrics: Rent, Vacancy │
│ │
│ ┌───────────────────────────────────────────────────────┐ │
│ │ City Neighbourhoods Overlay │ │
│ │ (158 boundaries as reference layer) │ │
│ │ + Enrichment data (density, education, income) │ │
│ ──────────────────────────────────────────────────────────┘ │
│ │
────────────────────────────────────────────────────────────────────┘

Enrichment Integration Strategy (Phased)

V1: Reference Overlay (Current Scope)

Approach: Display neighbourhood enrichment as a separate toggle-able layer. No joins to housing data.

UX:

• User hovers over TRREB district → tooltip shows "This district contains neighbourhoods: Annex, Casa Loma, Yorkville..."
• User toggles "Show Enrichment" → choropleth switches to neighbourhood-level density/education/income
• Enrichment and housing metrics displayed side-by-side, not merged

Pros:

• No imputation or dodgy aggregations
• Honest about geographic mismatch
• Ships faster

Cons:

• Can't do correlation analysis (price vs. enrichment) directly in dashboard

Implementation:

• dim_neighbourhood as standalone dimension (no FK to fact tables)
• Spatial lookup on hover (point-in-polygon)

V2/Portfolio Phase 4: Area-Weighted Aggregation (Future Scope)

Approach: Pre-compute area-weighted averages of neighbourhood metrics for each TRREB district and CMHC zone.

Process:

1. Spatial join: intersect neighbourhood polygons with TRREB/CMHC polygons
2. Compute overlap area for each neighbourhood-district pair
3. Weight neighbourhood metrics by overlap area proportion
4. User selects aggregation method in UI

Aggregation Methods to Expose:

Method	Description	Best For
Area-weighted mean	Weight by % overlap area	Continuous metrics (density)
Population-weighted mean	Weight by population in overlap	Per-capita metrics (education)
Majority assignment	Assign neighbourhood to district with >50% overlap	Categorical data
Max overlap	Assign to single district with largest overlap	1:1 mapping needs

Default: Population-weighted (more defensible for per-capita metrics). Hide selector behind "Advanced" toggle.

V1 Future-Proofing (Do Now)

Action	Why
Store neighbourhood boundaries in same CRS as TRREB/CMHC (WGS84)	Avoids reprojection headaches
Keep dim_neighbourhood normalized (not denormalized into district tables)	Clean separation for V2 join
Document Census year for each metric	Ready for 2026 Census
Include census_year column in dim_neighbourhood	Enables SCD tracking

V1 Defer (Don't Do Yet)

Action	Why Not
Pre-compute area-weighted crosswalk	Don't need for V1
Build aggregation method selector UI	No backend to support it
Crime data integration	Deferred to Portfolio Phase 4
Historical neighbourhood boundary reconciliation (140→158)	Use 2021+ data only for V1

---

Proposed Data Model

Star Schema

┌──────────────────┐
│ dim_time │
├──────────────────┤
│ date_key (PK) │
│ year │
│ month │
│ quarter │
│ month_name │
───────────────────────┘
│
┌─────────────────────────────────────────────┐
│ │ │
│
┌──────────────────┐ │ ┌──────────────────┐
│ dim_trreb_district│ │ │ dim_cmhc_zone │
├──────────────────┤ │ ├──────────────────┤
│ district_key (PK)│ │ │ zone_key (PK) │
│ district_code │ │ │ zone_code │
│ district_name │ │ │ zone_name │
│ area_type │ │ │ geometry │
│ geometry │
───────────────────────┘ │ │
│ │ │
│
┌──────────────────┐ │ ┌──────────────────┐
│ fact_purchases │ │ │ fact_rentals │
├──────────────────┤ │ ├──────────────────┤
│ date_key (FK) │ │ │ date_key (FK) │
│ district_key (FK)│ │ │ zone_key (FK) │
│ sales_count │ │ │ bedroom_type │
│ avg_price │ │ │ avg_rent │
│ median_price │ │ │ median_rent │
│ new_listings │ │ │ vacancy_rate │
│ active_listings │ │ │ universe │
│ avg_dom │ │ │ turnover_rate │
│ avg_sp_lp │ │ │ reliability_code │
─────────────────────┘ │ ─────────────────────┘
│

┌───────────────────────────┐
│ dim_neighbourhood │
├───────────────────────────┤
│ neighbourhood_id (PK) │
│ name │
│ geometry │
│ population │
│ land_area_sqkm │
│ pop_density_per_sqkm │
│ pct_bachelors_or_higher │
│ median_household_income │
│ pct_owner_occupied │
│ pct_renter_occupied │
│ census_year │ ← For SCD tracking
──────────────────────────────┘

┌───────────────────────────┐
│ dim_policy_event │
├───────────────────────────┤
│ event_id (PK) │
│ event_date │
│ effective_date │
│ level │ ← federal/provincial/municipal
│ category │ ← monetary/tax/regulatory/supply/economic
│ title │
│ description │
│ expected_direction │ ← bearish/bullish/neutral
│ source_url │
│ confidence │ ← high/medium/low
│ created_at │
──────────────────────────────┘

┌───────────────────────────┐
│ bridge_district_neighbourhood │ ← Portfolio Phase 4 ONLY
├───────────────────────────┤
│ district_key (FK) │
│ neighbourhood_id (FK) │
│ area_overlap_pct │
│ population_overlap │ ← For pop-weighted agg
──────────────────────────────┘

Notes:

• dim_neighbourhood has no FK relationship to fact tables in V1
• dim_policy_event is standalone (no FK to facts); used for time-series annotation
• bridge_district_neighbourhood is Portfolio Phase 4 scope only
• Similar bridge table needed for CMHC zones in Portfolio Phase 4

---

File Structure

Note

: Toronto Housing data logic lives in portfolio_app/toronto/. See portfolio_project_plan_v5.md for full project structure.

Data Directory Structure

data/
└── toronto/
    ├── raw/
    │   ├── trreb/
    │   │   └── market_watch_YYYY_MM.pdf
    │   ├── cmhc/
    │   │   └── rental_survey_YYYY.csv
    │   ├── enrichment/
    │   │   └── neighbourhood_profiles_2021.xlsx
    │   └── geo/
    │       ├── toronto_neighbourhoods.geojson
    │       ├── trreb_districts.geojson        ← (to be created via QGIS)
    │       └── cmhc_zones.geojson             ← (from R cmhc package)
    │
    ├── processed/                              ← gitignored
    │   ├── fact_purchases.parquet
    │   ├── fact_rentals.parquet
    │   ├── dim_time.parquet
    │   ├── dim_trreb_district.parquet
    │   ├── dim_cmhc_zone.parquet
    │   ├── dim_neighbourhood.parquet
    │   └── dim_policy_event.parquet
    │
    └── reference/
        ├── policy_events.csv                   ← Curated event list
        └── neighbourhood_boundary_changelog.md ← 140→158 notes

Code Module Structure

portfolio_app/toronto/
├── __init__.py
├── parsers/
│   ├── __init__.py
│   ├── trreb.py                    # PDF extraction
│   └── cmhc.py                     # CSV processing
├── loaders/
│   ├── __init__.py
│   └── database.py                 # DB operations
├── schemas/                        # Pydantic models
│   ├── __init__.py
│   ├── trreb.py
│   ├── cmhc.py
│   ├── enrichment.py
│   └── policy_event.py
├── models/                         # SQLAlchemy ORM
│   ├── __init__.py
│   ├── base.py                     # DeclarativeBase, engine
│   ├── dimensions.py               # dim_time, dim_trreb_district, dim_policy_event, etc.
│   └── facts.py                    # fact_purchases, fact_rentals
└── transforms/
    └── __init__.py

Notebooks

notebooks/
├── 01_trreb_pdf_extraction.ipynb
├── 02_cmhc_data_prep.ipynb
├── 03_geo_layer_prep.ipynb
├── 04_enrichment_data_prep.ipynb
├── 05_policy_events_curation.ipynb
└── 06_spatial_crosswalk.ipynb          ← Portfolio Phase 4 only

---

✅ Implementation Checklist

Note

: These are Stages within the Toronto Housing project (Portfolio Phase 1). They are distinct from the overall portfolio Phases defined in portfolio_project_plan_v5.md.

Stage 1: Data Acquisition

• Download TRREB monthly PDFs (2020-present as MVP)
• Register for CMHC portal and export Toronto rental data
• Extract CMHC zone boundaries via R cmhc package
• Download City of Toronto neighbourhood GeoJSON (158 boundaries)
• Digitize TRREB district boundaries in QGIS
• Download Neighbourhood Profiles (2021 Census, 158-model)

Stage 2: Data Processing

• Build TRREB PDF parser (portfolio_app/toronto/parsers/trreb.py)
• Build Pydantic schemas (portfolio_app/toronto/schemas/)
• Build SQLAlchemy models (portfolio_app/toronto/models/)
• Extract and validate TRREB monthly summaries
• Clean and structure CMHC rental data
• Process Neighbourhood Profiles into dim_neighbourhood
• Curate and load policy events into dim_policy_event
• Create dimension tables
• Build fact tables
• Validate all geospatial layers use same CRS (WGS84/EPSG:4326)

Stage 3: Visualization (V1)

• Create dashboard page (portfolio_app/pages/toronto/dashboard.py)
• Build choropleth figures (portfolio_app/figures/choropleth.py)
• Build time series figures (portfolio_app/figures/time_series.py)
• Design dashboard layout (purchase/rental toggle)
• Implement choropleth map with layer switching
• Add time slider/selector
• Build neighbourhood overlay (toggle-able)
• Add enrichment layer toggle (density/education/income choropleth)
• Add policy event markers on time series
• Add tooltips with cross-reference info ("This district contains...")
• Add tooltips showing enrichment metrics on hover

Stage 4: Polish (V1)

• Add data source citations
• Document methodology (especially geographic limitations)
• Write docs (docs/methodology.md, docs/data_sources.md)
• Deploy to portfolio

Future Enhancements (Portfolio Phase 4 — Post-Energy Project)

• Add crime data to dim_neighbourhood
• Build spatial crosswalk (neighbourhood ↔ district/zone intersections)
• Compute area-weighted and population-weighted aggregations
• Add aggregation method selector to UI
• Enable correlation analysis (price vs. enrichment metrics)
• Add historical neighbourhood boundary support (140→158)

Deployment & dbt Architecture: See portfolio_project_plan_v5.md for:

• dbt layer structure and testing strategy
• Deployment architecture
• Data quality framework

---

References & Links

Core Housing Data

Resource	URL
TRREB Market Watch	https://trreb.ca/index.php/market-news/market-watch
CMHC Housing Portal	https://www03.cmhc-schl.gc.ca/hmip-pimh/

Geographic Boundaries

Resource	URL
Toronto Neighbourhoods GeoJSON	https://github.com/jasonicarter/toronto-geojson
TRREB District Map (PDF)	https://webapp.proptx.ca/trrebdata/common/maps/Toronto.pdf
Statistics Canada Census Tracts	https://www12.statcan.gc.ca/census-recensement/2021/geo/sip-pis/boundary-limites/index-eng.cfm
R cmhc package (CRAN)	https://cran.r-project.org/package=cmhc

Enrichment Data

Resource	URL
Toronto Open Data Portal	https://open.toronto.ca/
Neighbourhood Profiles (CKAN)	https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/neighbourhood-profiles
Neighbourhood Profiles 2021 (Direct Download)	https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/6e19a90f-971c-46b3-852c-0c48c436d1fc/resource/19d4a806-7385-4889-acf2-256f1e079060/download/nbhd_2021_census_profile_full_158model.xlsx

Policy Events Research

Resource	URL
Bank of Canada Interest Rates	https://www.bankofcanada.ca/rates/interest-rates/
OSFI (Stress Test Rules)	https://www.osfi-bsif.gc.ca/
Ontario Legislature (Bills)	https://www.ola.org/

Reference Documentation

Resource	URL
Statistics Canada 2021 Census Reference	https://www12.statcan.gc.ca/census-recensement/2021/ref/index-eng.cfm
City of Toronto Neighbourhood Profiles Overview	https://www.toronto.ca/city-government/data-research-maps/neighbourhoods-communities/neighbourhood-profiles/

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Related Documents

Document	Relationship	Use For
portfolio_project_plan_v5.md	Parent document	Overall scope, phasing, tech stack, deployment, dbt architecture, data quality framework

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Document Version: 5.1 Updated: January 2026 Project: Toronto Housing Price Dashboard — Portfolio Piece