Powered by Alpha De Luxe™, the interest rate, housing price prediction, and upgrade strategy are so accurate it's poetic; Shakespearean, nearly.
Figure 1. Exterior view of The Parker luxury condo, the data source for the 2buyornot2buy™ price prediction models.
- 📖 What is 2buyornot2buy™?
- 🔧 Powered by
- 🌀 How It Works
- 🌐 Macroeconomic Modules
- 🏙️ Incentive & Decision Modules
- 🚀 Quick Start
- 🗂️ Repo Structure
- 📐 Foundations
- 📓 Textbook vs Codebook
- 🤝 Contributing
- 🪪 License
A strategic decision engine for timing and structuring luxury condo acquisitions at The Parker in Boston. Got Shakespeare on your side, to buy or not to buy?
Answered with data.
Alpha De Luxe™
A unique, original, first-of-its-kind scaffold combining proprietary applied mathematics, probability-weighted scenario simulations, and machine learning.
This project integrates a modular set of trademarked modeling strategies designed for predictive clarity, equity optimization, and decision leverage:
- Power Moves ROI Optimization™ – Strategic umbrella for remodeling and location valuation
- Splash-to-Cash Strategy™ – Amenity-aware ROI modeling for pool installations
- Bang-for-Buck Remodeling Strategy™ – Tactical upgrade ROI analysis
- Micro-Alpha Scaffolding™ – Signal extraction from low-noise features
- Buy Signal Decision Engine™ – Final-stage recommendation logic
- Macroeconomic Scenario Engines™ – Forecasting, comparison, and probability modeling
- Upgrade Path & Lifetime Value™ – Long-term equity optimization
- The Money in Bonus Calculator™ – Incentive modeling for buyer psychology
- Life Elevator™ – Narrative wrapper for the full modeling journey
Extract Boston condo pricing, incentives, and macro data; transform into unified time series; load into ML-ready tables.
Explore price distributions, incentive impacts, and seasonal patterns with interactive visualizations.
Build focused sub-models on niche signals—like fringe market incentives—layered to amplify alpha.
Synthesize lagged mortgage rates, inflation adjustments, and neighborhood quality into predictive features.
Train ensemble regressors (Random Forest, XGBoost) on log-transformed price indices.
Simulate future housing price indices under user-defined interest-rate and inflation regimes.
Overlay historical data and multiple rate scenarios in one plot with clear seasonal markers.
Assign probabilities to each scenario based on historical patterns and market expectations.
Quantify how local closing-cost credits, rate buydowns, and amenity packages shift effective price.
Apply decision logic to forecasted scenarios, generating “buy now” or “wait” signals.
Model equity build-up and trade-up timing from studio to penthouse, under multiple appreciation rates.
Calculate net present value of developer incentives when negotiating purchase.
📉 Pre-April 2025 "Market Tarriff Tantrum Drag", my housing interest rate forecasted in March 2025: -6.18%
Alpha De Luxe™ priced in a somewhat unexpected stock market volatility event in April as a "tariff tantrum" reaction
- My forecast looks poised to land within 0.06% of ground truth (pending final Fed rate cut confirmation, 99% probable)
- Some tariff finalizations are still pending, with a 90-day extension active (e.g., China)
- As lightning doesn't strike twice, you can also check out my modern mercantilism dashboard, which appears poised to accurately predict an average 15% tariff rate across the board.
Predictions this robust are not mere good fortune —it’s latent signal clairvoyance.
🔗 Modern Mercantilism Dashboard
Average tariff rate now converging toward the 15% estimate embedded in the original model.
git clone https://github.com/GarrickPinon/2buyornot2buy.git
cd 2buyornot2buy
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
streamlit run app.py2buyornot2buy/
├── app.py # Streamlit dashboard
├── data/
│ ├── raw/ # Original price & macro CSVs
│ └── processed/ # Cleaned datasets
├── modules/
│ ├── etl.py # Extraction & transformation
│ ├── forecasting.py # ML pipelines & scenario sim
│ ├── decision_engine.py # Buy signal & negotiation logic
│ ├── incentive_calc.py # Move-in bonus + upgrade timeline
│ └── viz.py # Plot utilities
├── trademarks/
│ ├── LICENSE_IP.md # Proprietary module declarations
│ └── TRADEMARKS.md # Definitions + usage rights
├── notebooks/
│ └── alpha_sandbox.ipynb # Experimental scaffolding + EDA
├── README.md # Council-grade overview
├── requirements.txt # Dependencies
└── .streamlit/
└── config.toml # UI tweaks (theme, layout)
- Bayesian Inference – posterior weighting, belief updating, probabilistic scoring
- Monte Carlo Methods – stochastic sampling, uncertainty quantification
- Optimization – gradient-based tuning, regularization
-
Scenario Probability Calculation
Textbook View: P(scenario) = Σ(weight × likelihood) across macro paths
Codebook Execution:scenario_prob = sum(w * p for w, p in zip(weights, likelihoods)) -
Upgrade Timeline Estimation
Textbook View: T_upgrade = (Δ equity + concessions) / target unit delta
Codebook Execution:time_to_upgrade = (equity_gain + concessions) / unit_price_diff -
Buy Signal Logic
Textbook View: Signal = 1 if (NPV_future - NPV_now) > threshold
Codebook Execution:buy_signal = int((npv_future - npv_now) > trigger_threshold) -
Concession Value Extraction
Textbook View: Value = Σ(bonus × time_saved) across upgrade path
Codebook Execution:concession_value = sum(bonus * time_saved for bonus, time_saved in path)
We’re stronger together.
- Fork the repo
- Create your branch:
git checkout -b feature/your-feature - Commit your changes:
git commit -m 'Add amazing feature' - Push:
git push origin feature/your-feature - Open a PR and describe your enhancement
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🪪 License
MIT License. Includes proprietary modules listed in LICENSE_IP.md.
If you’re reading this, you’re not just curious—you’re committed. These badges are for you. Let's be friends.
