humbleworth/price-predict-v1

# humbleworth/price-predict-v1 – Replicate Source: [https://replicate.com/humbleworth/price-predict-v1](https://replicate.com/humbleworth/price-predict-v1) ## HumbleWorth Domain Price Prediction An AI\-powered domain name valuation model that provides instant price estimates across three sales channels: auction, marketplace, and brokerage\. This model powers the free HumbleWorth domain valuation service\. ## Model Description This neural network model combines domain embeddings with word embeddings to predict domain name values\. The architecture uses: - **Sentence Transformers**\(all\-MiniLM\-L12\-v2\) for text encoding - **Domain Tokenization**with intelligent word segmentation - **Multi\-layer Perceptron**prediction head with dropout regularization - **Probability Curve Fitting**using tanh functions to generate price distributions The model was trained on multiple large\-scale datasets including: \-[DNPric\.es](http://dnpric.es/)historical auction data \(3M\+ transactions over 20\+ years\) \- Dropped and bargain domain datasets ## Intended Use **Designed for:**\- Domain investors evaluating portfolio value \- Quick price estimates for domain acquisitions \- Bulk domain valuation \(up to 2560 domains per request\) \- Market research and competitive analysis \- Educational purposes understanding domain valuation **NOT designed for:**\- Financial advice or investment recommendations \- Legal valuation for official purposes \(court cases, tax assessments\) \- High\-frequency trading or arbitrage strategies \- Trademark or intellectual property valuations ## Usage ### Input - **domains**: Comma\-separated list of domain names \(max 2560\) - Example:`"example\.com,test\.net,domain\.org"` ### Output Returns structured JSON with valuations for each domain across three sales channels: ``` { "valuations": [ { "domain": "example.com", "auction": 1500.0, "marketplace": 5000.0, "brokerage": 8000.0, "error": null } ] } ``` ### Sales Channel Explanations - **Auction**: Estimated value in competitive auction environments \(50th percentile\) - **Marketplace**: Estimated value for direct marketplace sales \(97\.5th percentile\) - **Brokerage**: Estimated value through premium brokerage services \(99\.25th percentile\) ### API Compatibility This model provides enhanced functionality compared to the original HumbleWorth API: \- Same input/output format \- Increased batch limit \(2560 domains vs 20 domains\) \- Optimized batched processing for large requests \- Drop\-in replacement for`https://valuation\.humbleworth\.com/api/valuation` ## Usage Tips **Best Practices:**\- Submit domains in lowercase for consistency \- Batch multiple domains \(up to 2560\) for maximum efficiency \- Include TLD extensions \(\.com, \.net, etc\.\) \- Avoid special characters or internationalized domains **Input Quality:**\- Valid, registered domain formats work best \- Recently dropped domains may have less reliable estimates \- Brandable domains typically get higher valuations than generic keywords \- Shorter domains generally receive higher estimates **Processing Large Portfolios:**\- Single requests can handle up to 2560 domains efficiently \- Internal batching \(128 domains per batch\) optimizes memory usage \- Large requests may take longer but avoid rate limiting issues ## Ethical Considerations **Model Limitations:**\- Estimates are computer\-generated and should not replace human expertise \- Market conditions change rapidly; historical data may not reflect current trends \- Model may exhibit bias toward English\-language domains \- Valuations can vary significantly from actual sale prices **Responsible Use:**\- Always disclose that estimates are AI\-generated when sharing with others \- Consider multiple valuation sources for important decisions \- Understand that domain values are highly subjective and market\-dependent \- Use estimates as starting points for further research, not final assessments **Transparency:**\- Model predictions are based on historical sales data through early 2024 \- Training data may not represent all market segments equally \- Performance may vary for newer TLDs or emerging market trends ## Caveats and Recommendations **Technical Considerations:**\- Model optimized for CPU inference on small hardware \(2GB RAM\) \- Response times typically under 2 seconds for single domains \- Batch processing is more efficient than individual requests **Market Context:**\- Domain values are highly volatile and context\-dependent \- Economic conditions significantly impact domain market prices \- Trademark issues and legal considerations are not evaluated \- Seasonal trends and market cycles are not explicitly modeled **Data Freshness:**\- Training data extends through 2024 but may not capture very recent market shifts \- Emerging trends in domain markets may not be fully represented \- Consider supplementing with current market research for major decisions ## Examples **Single Domain:** ``` Input: "example.com" Output: {"valuations": [{"domain": "example.com", "auction": 1200, "marketplace": 3500, "brokerage": 5200}]} ``` **Multiple Domains:** ``` Input: "tech.com,startup.net,innovation.org" Output: {"valuations": [ {"domain": "tech.com", "auction": 15000, "marketplace": 45000, "brokerage": 75000}, {"domain": "startup.net", "auction": 800, "marketplace": 2400, "brokerage": 4000}, {"domain": "innovation.org", "auction": 600, "marketplace": 1800, "brokerage": 3000} ]} ``` --- *This model is part of the HumbleWorth ecosystem, providing free AI\-powered domain valuations\. With support for up to 2,560 domains per request, this Replicate model exceeds the bulk processing capability of[humbleworth\.com](https://humbleworth.com/)while offering programmatic access\.* Model created10 months, 3 weeks ago