Industries — Real Estate

Data & AI for Real Estate

Market intelligence, investment scoring, and price forecasting for Canadian real estate — powered by a live PySpark pipeline and ML models built on public data.

2026-Q1

Ottawa
NHPI 160.9
Affordability 26.4
Toronto
NHPI 107.8
Affordability 17.7
Montreal
NHPI 158.9
Affordability 26.1

Sources: Statistics Canada · Bank of Canada · Refreshed monthly via GitHub Actions · View full dashboard →

Built with Statistics Canada GitHub Actions PySpark Parquet Streamlit

Industry Challenges

The data challenges real estate organizations face

Real estate decisions move on imperfect data — fragmented sources, lagging indicators, and market sentiment that is hard to quantify.

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Fragmented market data

CMHC, CREA MLS, Statistics Canada, and Airbnb all use different formats, geographies, and update cadences.

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Lagging indicators

By the time MLS data is published and analyzed, market conditions have already shifted — investors need leading signals.

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Short-term rental market opacity

STR occupancy, pricing, and neighbourhood saturation are critical inputs for investment decisions but hard to collect at scale.

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Affordability and macro alignment

Mortgage rate changes, CPI, and income data must be combined with housing data to produce actionable affordability signals.

How We Help

How LanaCloud delivers results

We have already built a production Canadian real estate data pipeline — ingesting CMHC, CREA, Statistics Canada, and Inside Airbnb data into a gold-layer dataset ready for ML and dashboards.

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Medallion data pipeline

Bronze → Silver → Gold PySpark pipeline ingesting CMHC, CREA, Statistics Canada, Bank of Canada, and Inside Airbnb data automatically.

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Investment scoring models

ML classification model that scores each CMA as Buy / Hold / Avoid based on composite investment signals from the gold layer.

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Price forecasting

Regression models predicting median home price for the next quarter per city, trained on 5+ years of macro and housing data.

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STR market analysis

Occupancy rate, nightly price, and neighbourhood saturation scoring for Ottawa, Toronto, and Montreal STR markets.

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Interactive dashboards

Streamlit and Power BI dashboards showing price trends, affordability index, vacancy rates, and investment signals by CMA.

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Natural language market queries

Ask "What is the affordability index for Ottawa this quarter?" and get a live answer — powered by Claude + MCP over the gold layer.

Use Cases

Real-world applications

Built for real estate investors, developers, REITs, and property management companies operating in Canadian markets.

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REIT portfolio analytics

Automated dashboards tracking cap rates, NOI trends, and regional exposure across a multi-city portfolio.

BI · Portfolio
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Investment opportunity scoring

Buy / Hold / Avoid signals updated quarterly for 15+ Canadian CMAs, based on housing, macro, and STR data.

ML · Investment
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STR investment viability

Estimate expected occupancy, nightly rates, and monthly revenue for a new STR property in any tracked neighbourhood.

ML · STR
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Market sentiment NLP

Sentiment scoring of Airbnb guest reviews and real estate news headlines as leading indicators for neighbourhood trajectory.

NLP · Sentiment

Want to see the live Canadian real estate pipeline?

We can show you a live demo of the data pipeline, investment scoring model, and interactive dashboard.

LanaCloud Assistant
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