AIOZ AI Housing Prices Challenge: Rules, Scoring, Submission, Best Practice

Success in the Housing Prices Challenge goes beyond clever models — it's about playing fair, submitting correctly, and avoiding common traps like data leakage.

This guide breaks down the rules, evaluation mechanics, submission specs, and a leakage playbook to ensure your offline experiments align with leaderboard results.

With multiple daily entries (up to 5 per day), it's essential to focus on thoughtful iterations: Train on labeled data, make blind predictions, and validate rigorously.

Let's show you how to rise to the top ranks smoothly and ethically.

Evaluation: How Your Model Gets Scored

Your predictions are evaluated using Root Mean Squared Error (RMSE) on log-transformed prices (both predicted and actual). This metric rewards precision across scales — nailing a $50K apartment counts as much as a $5M estate.

Lower RMSE wins; it's straightforward and rewards robust variance handling.

• Why Log-Transform? It normalizes skewed prices, preventing high-end outliers from dominating.
• Offline tip: Mirror this in your code — log prices before RMSE calculation for accurate local evaluations.

Scores update on the Public Leaderboard after each submission, fueling community benchmarking.

Submission Guidelines: Get It Right the First Time

Keep it simple: A CSV with a header and exactly two columns.

The platform auto-scores, and mismatches (e.g., wrong IDs) result in a zero score.

• house_index: use exact matches from test.csv (69,649 entries).
• price: Submit prices in the original scale; the platform applies the log transform for RMSE.
• Upload via the AIOZ AI platform; expect near‑instant leaderboard feedback.

Rules Recap:

1. One account per participant — fair play only.
2. Share code and datasets only in public forums; avoid private exchanges.
3. Maximum of 5 submissions per day — quality over quantity.

We're building a collaborative space — lean on discussions for tips, not secrets.

Leakage Playbook: Safeguard Your Scores

Leakage can inflate offline results and tank live performance—typically by pulling “future” information or mixing pipelines. Stay clean with these:

1. Identifier Leakage: Use house_index only for alignment; exclude it from your features to avoid ID overfitting.
2. Incomputable Features: Derive inputs from fields available in both train and test; avoid statistics computed only on train (e.g., mean price by city from train alone).
3. Train-Test Contamination: Fit on train.csv exclusively; keep test.csv blind; do not access ground_truth.csv
4. Target Mismatch: Model "final sale price" — use purpose to filter sales, avoiding rent dilution.
5. Transform Mismatch: Submit raw prices; log offline to sync with scoring.

Pro tip: Split train.csv 80/20 for internal train/validation — treat validation like test for realism.

Quality Checklist: Pre-Submission Audit

Tick these for leaderboard-ready work:

• Train/validate on train.csv (with price); test.csv unlabeled only.
• Exclude house_index from the feature set.
• Apply identical preprocessing to train and test.
• Ensure the target corresponds to the final sale price (e.g., select sale via purpose).
• CSV format: header house_index, price; IDs matching test.csv; original‑scale prices.
• Offline RMSE: On log‑transformed prices for true comparisons.

Quick 5 Key Facts

1. Rows: 69,649 train, 29,850 test — diverse and doable.
2. Target: Final sale price (present only in train.csv).
3. Scoring: RMSE on log‑transformed prices — scale-agnostic.
4. Purpose: Sale/rent flag — vital for purity.
5. Submit: Original prices in house_index, price CSV.

Final Thoughts

Mastering rules and submissions turns potential pitfalls into power moves. Respect boundaries, validate smartly, and collaborate openly — this challenge is about shared growth in AI for real impact.

Submit your first model today, climb the ranks, and watch your predictions shape housing wisdom.

Join in at: aiozai.network/challenges — your clean run starts now!