Subnet Directory

With the amount of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time

Subnet 46

RESI

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ABOUT

What exactly does it do?

RESI (Real Estate Super Intelligence) is a decentralized AI subnet on the Bittensor network focused on building an open, real-time repository of property data and analytics. (Subnet 46 was originally known as NeuralAI, a 3D asset generation project, but after a charity auction in late 2024 it was acquired by Resi Labs and repurposed as RESI with a completely new real-estate focus.)

The goal of RESI is to create the world’s largest open real estate database by crowdsourcing data from many sources and making it accessible via modern APIs. It aims to eliminate the traditional barriers in the $45 trillion real estate industry, where data has been expensive, fragmented, and tightly controlled by a few providers. In simpler terms, RESI “unlocks” property data that was previously siloed or paywalled, allowing developers, researchers, and businesses to build novel AI-driven real estate applications on top of a comprehensive, up-to-date data platform.

Some key challenges in traditional real estate data that RESI addresses include:

High Cost of Data Access: Major real estate databases often charge exorbitant fees (on the order of $250,000+ per year for basic access), which prices out small businesses and innovators.

Fragmented Data Sources: Critical property information is scattered across thousands of county assessors, MLS systems, and proprietary databases, each requiring separate contracts or subscriptions. This fragmentation makes it hard to gather a complete picture without immense effort and cost.

Restrictive Licensing: Even if one can pay for the data, providers impose licensing terms that prevent the use of that data for AI training (for example, barring services like ChatGPT or Claude from learning from it). This protects incumbents’ monopolies but stifles AI innovation.

RESI eliminates these barriers by leveraging Bittensor’s decentralized network of miners and validators to continuously collect and verify property data at scale. In the RESI subnet, participants (miners) are incentivized to scrape data from any accessible source – from public records and county databases to real estate websites like Zillow or Redfin – and contribute that data to a shared knowledge base. The data is then validated by other participants (validators) in a consensus process to ensure accuracy and integrity before being added to the global database. This approach creates a living, real-time national property database that updates as the market moves, rather than a static dataset. Unlike closed corporate data silos, RESI’s open platform is designed to continuously learn and improve as more data is gathered and used, ultimately enabling AI capabilities that no single company could achieve alone. The initial scope is 150+ million properties across the USA, with plans to expand globally, making RESI a foundational infrastructure for next-generation real estate technology across all sectors.

 

RESI (Real Estate Super Intelligence) is a decentralized AI subnet on the Bittensor network focused on building an open, real-time repository of property data and analytics. (Subnet 46 was originally known as NeuralAI, a 3D asset generation project, but after a charity auction in late 2024 it was acquired by Resi Labs and repurposed as RESI with a completely new real-estate focus.)

The goal of RESI is to create the world’s largest open real estate database by crowdsourcing data from many sources and making it accessible via modern APIs. It aims to eliminate the traditional barriers in the $45 trillion real estate industry, where data has been expensive, fragmented, and tightly controlled by a few providers. In simpler terms, RESI “unlocks” property data that was previously siloed or paywalled, allowing developers, researchers, and businesses to build novel AI-driven real estate applications on top of a comprehensive, up-to-date data platform.

Some key challenges in traditional real estate data that RESI addresses include:

High Cost of Data Access: Major real estate databases often charge exorbitant fees (on the order of $250,000+ per year for basic access), which prices out small businesses and innovators.

Fragmented Data Sources: Critical property information is scattered across thousands of county assessors, MLS systems, and proprietary databases, each requiring separate contracts or subscriptions. This fragmentation makes it hard to gather a complete picture without immense effort and cost.

Restrictive Licensing: Even if one can pay for the data, providers impose licensing terms that prevent the use of that data for AI training (for example, barring services like ChatGPT or Claude from learning from it). This protects incumbents’ monopolies but stifles AI innovation.

RESI eliminates these barriers by leveraging Bittensor’s decentralized network of miners and validators to continuously collect and verify property data at scale. In the RESI subnet, participants (miners) are incentivized to scrape data from any accessible source – from public records and county databases to real estate websites like Zillow or Redfin – and contribute that data to a shared knowledge base. The data is then validated by other participants (validators) in a consensus process to ensure accuracy and integrity before being added to the global database. This approach creates a living, real-time national property database that updates as the market moves, rather than a static dataset. Unlike closed corporate data silos, RESI’s open platform is designed to continuously learn and improve as more data is gathered and used, ultimately enabling AI capabilities that no single company could achieve alone. The initial scope is 150+ million properties across the USA, with plans to expand globally, making RESI a foundational infrastructure for next-generation real estate technology across all sectors.

 

PURPOSE

What exactly is the 'product/build'?

RESI’s product is essentially a decentralized platform that collects, verifies, and serves real estate data on a massive scale. Technically, it is implemented as a Bittensor subnet where independent nodes (miners and validators) cooperate to gather property information and maintain a high-quality database. The architecture of RESI is built around incentivized data mining and rigorous validation:

Decentralised Data Collection: Miners in the RESI subnet compete to discover and submit property data from diverse sources (online listings, public records, etc.), earning rewards for contributing useful information. The system encourages comprehensive coverage by rewarding data at a granular field level (over 100+ property attributes are tracked) and giving bonuses for new or hard-to-find data (e.g. adding a previously unlisted property or a rare attribute). This means one miner might focus on scraping basic info (bedrooms, price, etc.) for thousands of homes, while another specializes in gathering niche data like building permits or HOA regulations. All submitted data is tagged with confidence metrics and timestamps so that the knowledge base remains current and can decay or update over time as things change.

Consensus & Validation: Validators play a critical role in ensuring data quality and preventing bad actors. RESI uses a blind consensus mechanism – miners cannot see each other’s submissions during the validation process – and requires multiple miners/validators to independently agree on each data point. For example, if a miner submits that “123 Main St, Beverly Hills, sold for $5M on Jan 1”, other miners/validators must corroborate that fact (e.g. via another source or prior data) before it’s accepted. The network tracks a confidence score for every field in the database, adjusting rewards based on reliability. There are also measures like property existence checks (random spot-checking to ensure miners aren’t inventing fake properties) to preserve integrity. This multi-layered validation consensus makes it very difficult to game the system, since collusion or false data will be caught and becomes unprofitable.

Real-Time Updates: The platform is designed as a “living” database that updates continuously as new information comes in. Miners are incentivized not just to add new properties, but also to monitor changes in existing ones. For instance, “delta hunters” are miners rewarded for quickly detecting updates like ownership changes, new sales, or market listings. A maintenance mode ensures that the data on all 150M+ U.S. properties stays fresh, with miners periodically reconfirming details and updating records with low latency. Thanks to this decentralized monitoring, the system can propagate important changes (a property sold, a price change, a new construction permit, etc.) in near real-time, rather than relying on quarterly or yearly data refreshes common in traditional databases.

Incentive Structure: RESI’s tokenomics (built on Bittensor’s TAO emission model) are tuned to encourage a variety of mining strategies, so that no aspect of data collection is neglected. Different categories of miners earn specified portions of the subnet’s TAO emissions: for example, “Volume miners” focusing on large-scale scraping of listing sites might receive ~30% of rewards, whereas “Specialist miners” targeting detailed local records (tax assessments, deeds, etc.) get ~20%. Other roles include “Delta hunters” (~15%, for real-time change detection), “Maintenance miners” (~12.5%, for keeping data updated), corresponding maintenance validators (~12.5%), and “rare field” miners (~10%, for contributing uncommon data like lien information or off-market intel). By allocating rewards in this manner, RESI ensures an ecosystem of specialists — each motivated to cover different facets of real estate data — all feeding into the unified platform.

User Access & API: The end-product of RESI’s backend is exposed to users (developers, businesses, and even potentially end-users) via a modern API and web interface. Resi Labs plans to offer an API service where anyone can query property data or analytics at a very affordable cost (the roadmap suggests pricing on the order of $0.001 per basic property query via the API). This is a dramatic reduction compared to traditional data providers, aligning with the project’s mission to democratize access. The API and tools are being built to be developer-friendly – featuring field-level confidence scores (so you know how reliable each piece of data is) and fast response times (sub-200ms for queries, enabling real-time applications). A limited preview web app (demo.resilabs.ai) is expected to allow interactive testing of the data, with a full production API launch planned in Phase 1 of the roadmap.

Example Applications: The rich, open dataset that RESI produces can power many PropTech (property technology) applications. Resi Labs is already developing a flagship AI application for “seller intent prediction”, which uses machine learning on the RESI data to predict which homeowners are likely to sell their property in the near future. By analyzing patterns in ownership history, financial records, life events, and market trends, the model can identify subtle selling signals that traditional methods miss, giving real estate investors or agents a data-driven edge in approaching potential sellers. Beyond that, the open data enables a wide range of new tools and services. For example, third-party developers could build market analysis dashboards (scoring investment opportunities or spotting trends), CRM integrations for realtors to enrich their client data, legal due-diligence tools (for title companies or attorneys to quickly gather property records), and financial underwriting models for lenders to better evaluate collateral. All of these innovations are made possible by having a real-time, comprehensive property database that anyone can tap into, which previously was infeasible when data was locked away in silos.

In summary, the RESI “product” is not a single app, but a platform: a decentralized data collection network + a continuously updated real estate database + accessible APIs/analytics on top of that data. It transforms how real estate information is gathered and consumed, shifting from closed, expensive datasets to an open network where any participant can contribute data and any developer can build applications on it. This is underpinned by Bittensor’s blockchain, which provides the incentive layer (rewarding useful contributions with $TAO tokens) and the infrastructure for miners/validators to coordinate securely.

 

RESI’s product is essentially a decentralized platform that collects, verifies, and serves real estate data on a massive scale. Technically, it is implemented as a Bittensor subnet where independent nodes (miners and validators) cooperate to gather property information and maintain a high-quality database. The architecture of RESI is built around incentivized data mining and rigorous validation:

Decentralised Data Collection: Miners in the RESI subnet compete to discover and submit property data from diverse sources (online listings, public records, etc.), earning rewards for contributing useful information. The system encourages comprehensive coverage by rewarding data at a granular field level (over 100+ property attributes are tracked) and giving bonuses for new or hard-to-find data (e.g. adding a previously unlisted property or a rare attribute). This means one miner might focus on scraping basic info (bedrooms, price, etc.) for thousands of homes, while another specializes in gathering niche data like building permits or HOA regulations. All submitted data is tagged with confidence metrics and timestamps so that the knowledge base remains current and can decay or update over time as things change.

Consensus & Validation: Validators play a critical role in ensuring data quality and preventing bad actors. RESI uses a blind consensus mechanism – miners cannot see each other’s submissions during the validation process – and requires multiple miners/validators to independently agree on each data point. For example, if a miner submits that “123 Main St, Beverly Hills, sold for $5M on Jan 1”, other miners/validators must corroborate that fact (e.g. via another source or prior data) before it’s accepted. The network tracks a confidence score for every field in the database, adjusting rewards based on reliability. There are also measures like property existence checks (random spot-checking to ensure miners aren’t inventing fake properties) to preserve integrity. This multi-layered validation consensus makes it very difficult to game the system, since collusion or false data will be caught and becomes unprofitable.

Real-Time Updates: The platform is designed as a “living” database that updates continuously as new information comes in. Miners are incentivized not just to add new properties, but also to monitor changes in existing ones. For instance, “delta hunters” are miners rewarded for quickly detecting updates like ownership changes, new sales, or market listings. A maintenance mode ensures that the data on all 150M+ U.S. properties stays fresh, with miners periodically reconfirming details and updating records with low latency. Thanks to this decentralized monitoring, the system can propagate important changes (a property sold, a price change, a new construction permit, etc.) in near real-time, rather than relying on quarterly or yearly data refreshes common in traditional databases.

Incentive Structure: RESI’s tokenomics (built on Bittensor’s TAO emission model) are tuned to encourage a variety of mining strategies, so that no aspect of data collection is neglected. Different categories of miners earn specified portions of the subnet’s TAO emissions: for example, “Volume miners” focusing on large-scale scraping of listing sites might receive ~30% of rewards, whereas “Specialist miners” targeting detailed local records (tax assessments, deeds, etc.) get ~20%. Other roles include “Delta hunters” (~15%, for real-time change detection), “Maintenance miners” (~12.5%, for keeping data updated), corresponding maintenance validators (~12.5%), and “rare field” miners (~10%, for contributing uncommon data like lien information or off-market intel). By allocating rewards in this manner, RESI ensures an ecosystem of specialists — each motivated to cover different facets of real estate data — all feeding into the unified platform.

User Access & API: The end-product of RESI’s backend is exposed to users (developers, businesses, and even potentially end-users) via a modern API and web interface. Resi Labs plans to offer an API service where anyone can query property data or analytics at a very affordable cost (the roadmap suggests pricing on the order of $0.001 per basic property query via the API). This is a dramatic reduction compared to traditional data providers, aligning with the project’s mission to democratize access. The API and tools are being built to be developer-friendly – featuring field-level confidence scores (so you know how reliable each piece of data is) and fast response times (sub-200ms for queries, enabling real-time applications). A limited preview web app (demo.resilabs.ai) is expected to allow interactive testing of the data, with a full production API launch planned in Phase 1 of the roadmap.

Example Applications: The rich, open dataset that RESI produces can power many PropTech (property technology) applications. Resi Labs is already developing a flagship AI application for “seller intent prediction”, which uses machine learning on the RESI data to predict which homeowners are likely to sell their property in the near future. By analyzing patterns in ownership history, financial records, life events, and market trends, the model can identify subtle selling signals that traditional methods miss, giving real estate investors or agents a data-driven edge in approaching potential sellers. Beyond that, the open data enables a wide range of new tools and services. For example, third-party developers could build market analysis dashboards (scoring investment opportunities or spotting trends), CRM integrations for realtors to enrich their client data, legal due-diligence tools (for title companies or attorneys to quickly gather property records), and financial underwriting models for lenders to better evaluate collateral. All of these innovations are made possible by having a real-time, comprehensive property database that anyone can tap into, which previously was infeasible when data was locked away in silos.

In summary, the RESI “product” is not a single app, but a platform: a decentralized data collection network + a continuously updated real estate database + accessible APIs/analytics on top of that data. It transforms how real estate information is gathered and consumed, shifting from closed, expensive datasets to an open network where any participant can contribute data and any developer can build applications on it. This is underpinned by Bittensor’s blockchain, which provides the incentive layer (rewarding useful contributions with $TAO tokens) and the infrastructure for miners/validators to coordinate securely.

 

WHO

Team Info

Resi Labs (the team behind RESI subnet 46) is currently led by a small core team with expertise in both blockchain incentives and real estate data systems:

Seby Rubino – Principal and Project Lead. Seby is described as a crypto incentives and real estate expert, guiding RESI’s vision of merging decentralized economics with the needs of the real estate industry. (On social media he goes by “@sebyverse,” where he shares updates on RESI’s development.) Seby’s role involves overall strategy, ensuring that the subnet’s economic design effectively drives the collection of high-value property data and that the project addresses real estate industry pain points.

Caleb Gates – Technical Lead (AI Developer). Caleb is an experienced developer with a background in national real estate data systems. He oversees the engineering side of RESI, from building the data pipeline and consensus mechanisms to developing the APIs and AI models that utilize the data. Caleb’s experience with large-scale property datasets is particularly valuable for architecting RESI’s database and ensuring it can scale to hundreds of millions of records.

The team is actively growing – Resi Labs has indicated they are recruiting additional core developers, especially those familiar with Bittensor and decentralized AI, to help build out the platform. Given the breadth of the project (spanning blockchain, data scraping, machine learning, and API development), the team will likely expand to include specialists in those areas. The community is also an important part of the project’s development: since RESI is open-source and built “by the community for the community,” contributors from the wider Bittensor and PropTech communities are encouraged to participate. Stakeholders can follow progress or get involved through RESI’s official channels, including the @resilabsai account on X (Twitter) for updates and the Resi Labs GitHub repositories for code and documentation.

 

Resi Labs (the team behind RESI subnet 46) is currently led by a small core team with expertise in both blockchain incentives and real estate data systems:

Seby Rubino – Principal and Project Lead. Seby is described as a crypto incentives and real estate expert, guiding RESI’s vision of merging decentralized economics with the needs of the real estate industry. (On social media he goes by “@sebyverse,” where he shares updates on RESI’s development.) Seby’s role involves overall strategy, ensuring that the subnet’s economic design effectively drives the collection of high-value property data and that the project addresses real estate industry pain points.

Caleb Gates – Technical Lead (AI Developer). Caleb is an experienced developer with a background in national real estate data systems. He oversees the engineering side of RESI, from building the data pipeline and consensus mechanisms to developing the APIs and AI models that utilize the data. Caleb’s experience with large-scale property datasets is particularly valuable for architecting RESI’s database and ensuring it can scale to hundreds of millions of records.

The team is actively growing – Resi Labs has indicated they are recruiting additional core developers, especially those familiar with Bittensor and decentralized AI, to help build out the platform. Given the breadth of the project (spanning blockchain, data scraping, machine learning, and API development), the team will likely expand to include specialists in those areas. The community is also an important part of the project’s development: since RESI is open-source and built “by the community for the community,” contributors from the wider Bittensor and PropTech communities are encouraged to participate. Stakeholders can follow progress or get involved through RESI’s official channels, including the @resilabsai account on X (Twitter) for updates and the Resi Labs GitHub repositories for code and documentation.

 

FUTURE

Roadmap

RESI’s development roadmap is outlined in multiple phases, each focusing on expanding the data platform and delivering new capabilities:

Phase 1: Data Foundation – API Launch

This initial phase establishes the core infrastructure and data availability of the RESI network. Key objectives include:

  • National Property Database: Populate the platform with 150 million+ U.S. residential properties, covering core attributes for each (e.g. location, size, ownership, valuation, etc.). This creates the foundational dataset on which all services will build.
  • Multi-Source Aggregation: Enable miners to pull data from any accessible source (public databases, web APIs, etc.), integrating these inputs into a unified dataset. The focus is on breadth of coverage and combining data from many silos.
  • Consensus & Quality Mechanisms: Implement the basic field-level validation rules and anti-gaming protections in the network. At this stage, the emphasis is on making sure the data entering the system is vetted by multiple parties and that mechanisms like blind validation and confidence scoring are functioning to maintain data integrity.
  • Developer Preview: Provide a limited-access web interface or demo to let developers and early users explore and evaluate the data. This likely involves a beta version of the RESI API or a dashboard where one can query property information, thereby gathering feedback and showcasing the platform’s potential.

 

(The completion of Phase 1 essentially means RESI has a working decentralized data pipeline and a basic service for users to fetch data, proving out the concept of an open real estate database.)

 

Phase 2: Platform Applications – “Predict Casa” Launch (v2)

Phase 2 builds on the data foundation by introducing advanced AI applications and broadening the platform’s usability. Goals in this phase include:

  • Flagship AI Products: Launch RESI’s own value-added applications on the data. The roadmap specifically mentions deploying tools like seller intent prediction (Predict.Casa), more advanced market analysis tools, and an AI-enhanced CRM for real estate professionals. These products will demonstrate the power of the data by providing insights (e.g., predicting who is likely to sell, identifying investment opportunities, etc.) that weren’t possible before.
  • API Ecosystem: Open up the platform for external developers by fully launching the RESI API and related developer resources. This involves documentation, SDKs, and possibly a developer portal, allowing third parties to build their own applications on top of RESI data. By enabling outside innovation, RESI can accelerate the creation of diverse PropTech solutions.
  • Compute-to-Data Services: Introduce mechanisms for secure AI model training on the data without exposing the raw data. In other words, provide a way for AI developers to bring algorithms to the data (“compute-to-data”) so they can train models (for valuation, risk scoring, etc.) within the RESI framework, respecting privacy or licensing constraints of certain datasets.
  • Revenue Generation: Begin transitioning the project from being solely supported by Bittensor token emissions to having sustainable revenue streams. This means starting to monetize the API or applications (e.g. subscription fees, query fees beyond a free tier) and gaining real users, which will validate the economic model beyond the crypto incentives.

 

Phase 3: Global Expansion

In the final outlined phase, RESI aims to expand both the scope of data and its market reach to solidify its position as a global real estate intelligence network:

  • Commercial & Industrial Data: Extend the platform beyond residential properties to include commercial real estate – such as office buildings, retail spaces, warehouses, etc.. These sectors involve different data (leases, cap rates, zoning, etc.), so miners may start covering county records and databases relevant to commercial properties.
  • International Coverage: Scale out from the U.S. to global property data collection. This would entail adapting the mining and validation approach to different countries’ public records and real estate systems, and possibly partnering with local contributors in various regions. The ultimate vision is a worldwide open database of real estate information.
  • Enterprise Partnerships: Form white-label or enterprise partnerships with large organizations. For instance, RESI could power the back-end data for real estate websites, financial institutions, or government agencies looking to leverage comprehensive property data without building their own infrastructure. Such partnerships could drive significant usage and credibility, integrating RESI’s intelligence into mainstream platforms.
  • Training Marketplace: Evolve the network into a marketplace where miners/validators not only provide data but also offer specialized AI training services using RESI’s data. In practice, this could mean if a company wants a custom AI model (say, to appraise properties or forecast market trends), they could tap into the RESI subnet’s miners to train that model on up-to-date data, for a fee. This creates an additional economy around the data, where expertise in model building and data insights becomes a service provided by the community.

 

Each phase of the roadmap builds on the previous one, and together they outline a path from bootstrapping the data network (Phase 1) to demonstrating real-world utility (Phase 2) to scaling impact and adoption (Phase 3). The roadmap also reflects a shift from reliance on crypto incentives to mainstream viability: by Phase 3, RESI aims to be an indispensable infrastructure in the real estate industry, sustained by real users and partners, effectively “crossing the chasm” from a blockchain experiment to a widely-used open data platform. Throughout these phases, the team will likely continue iterating on the consensus algorithms and reward models to ensure data quality and network health as it grows. The vision driving all this is, as stated by the team, to “create the world’s most comprehensive open real estate intelligence network that powers the next generation of PropTech innovation” – essentially turning Subnet 46 into the de facto decentralized source of truth for property data worldwide.

 

RESI’s development roadmap is outlined in multiple phases, each focusing on expanding the data platform and delivering new capabilities:

Phase 1: Data Foundation – API Launch

This initial phase establishes the core infrastructure and data availability of the RESI network. Key objectives include:

  • National Property Database: Populate the platform with 150 million+ U.S. residential properties, covering core attributes for each (e.g. location, size, ownership, valuation, etc.). This creates the foundational dataset on which all services will build.
  • Multi-Source Aggregation: Enable miners to pull data from any accessible source (public databases, web APIs, etc.), integrating these inputs into a unified dataset. The focus is on breadth of coverage and combining data from many silos.
  • Consensus & Quality Mechanisms: Implement the basic field-level validation rules and anti-gaming protections in the network. At this stage, the emphasis is on making sure the data entering the system is vetted by multiple parties and that mechanisms like blind validation and confidence scoring are functioning to maintain data integrity.
  • Developer Preview: Provide a limited-access web interface or demo to let developers and early users explore and evaluate the data. This likely involves a beta version of the RESI API or a dashboard where one can query property information, thereby gathering feedback and showcasing the platform’s potential.

 

(The completion of Phase 1 essentially means RESI has a working decentralized data pipeline and a basic service for users to fetch data, proving out the concept of an open real estate database.)

 

Phase 2: Platform Applications – “Predict Casa” Launch (v2)

Phase 2 builds on the data foundation by introducing advanced AI applications and broadening the platform’s usability. Goals in this phase include:

  • Flagship AI Products: Launch RESI’s own value-added applications on the data. The roadmap specifically mentions deploying tools like seller intent prediction (Predict.Casa), more advanced market analysis tools, and an AI-enhanced CRM for real estate professionals. These products will demonstrate the power of the data by providing insights (e.g., predicting who is likely to sell, identifying investment opportunities, etc.) that weren’t possible before.
  • API Ecosystem: Open up the platform for external developers by fully launching the RESI API and related developer resources. This involves documentation, SDKs, and possibly a developer portal, allowing third parties to build their own applications on top of RESI data. By enabling outside innovation, RESI can accelerate the creation of diverse PropTech solutions.
  • Compute-to-Data Services: Introduce mechanisms for secure AI model training on the data without exposing the raw data. In other words, provide a way for AI developers to bring algorithms to the data (“compute-to-data”) so they can train models (for valuation, risk scoring, etc.) within the RESI framework, respecting privacy or licensing constraints of certain datasets.
  • Revenue Generation: Begin transitioning the project from being solely supported by Bittensor token emissions to having sustainable revenue streams. This means starting to monetize the API or applications (e.g. subscription fees, query fees beyond a free tier) and gaining real users, which will validate the economic model beyond the crypto incentives.

 

Phase 3: Global Expansion

In the final outlined phase, RESI aims to expand both the scope of data and its market reach to solidify its position as a global real estate intelligence network:

  • Commercial & Industrial Data: Extend the platform beyond residential properties to include commercial real estate – such as office buildings, retail spaces, warehouses, etc.. These sectors involve different data (leases, cap rates, zoning, etc.), so miners may start covering county records and databases relevant to commercial properties.
  • International Coverage: Scale out from the U.S. to global property data collection. This would entail adapting the mining and validation approach to different countries’ public records and real estate systems, and possibly partnering with local contributors in various regions. The ultimate vision is a worldwide open database of real estate information.
  • Enterprise Partnerships: Form white-label or enterprise partnerships with large organizations. For instance, RESI could power the back-end data for real estate websites, financial institutions, or government agencies looking to leverage comprehensive property data without building their own infrastructure. Such partnerships could drive significant usage and credibility, integrating RESI’s intelligence into mainstream platforms.
  • Training Marketplace: Evolve the network into a marketplace where miners/validators not only provide data but also offer specialized AI training services using RESI’s data. In practice, this could mean if a company wants a custom AI model (say, to appraise properties or forecast market trends), they could tap into the RESI subnet’s miners to train that model on up-to-date data, for a fee. This creates an additional economy around the data, where expertise in model building and data insights becomes a service provided by the community.

 

Each phase of the roadmap builds on the previous one, and together they outline a path from bootstrapping the data network (Phase 1) to demonstrating real-world utility (Phase 2) to scaling impact and adoption (Phase 3). The roadmap also reflects a shift from reliance on crypto incentives to mainstream viability: by Phase 3, RESI aims to be an indispensable infrastructure in the real estate industry, sustained by real users and partners, effectively “crossing the chasm” from a blockchain experiment to a widely-used open data platform. Throughout these phases, the team will likely continue iterating on the consensus algorithms and reward models to ensure data quality and network health as it grows. The vision driving all this is, as stated by the team, to “create the world’s most comprehensive open real estate intelligence network that powers the next generation of PropTech innovation” – essentially turning Subnet 46 into the de facto decentralized source of truth for property data worldwide.

 

MEDIA

Novelty Search is great, but for most investors trying to understand Bittensor, the technical depth is a wall, not a bridge. If we’re going to attract investment into this ecosystem then we need more people to understand it! That’s why Siam Kidd and Mark Creaser from DSV Fund have launched Revenue Search, where they ask the simple questions that investors want to know the answers to.

Recorded in September 2025, this Revenue Search episode features Seby Rubino, the new owner of Subnet 46, RESI. With a background in real estate, proptech, and DeFi, Seby explains how RESI is building a decentralized nationwide property database to rival centralized incumbents like Atom Data. This foundation will serve as an “oracle” for real estate, enabling accurate price feeds, AI-powered appraisals, and eventually on-chain lending products, including a real estate–backed stablecoin. The conversation covers RESI’s roadmap—from monetizing predictive lead-generation tools already in use by realtors, to enabling real estate prediction markets, to scaling globally with AI appraisers and loan products. Seby also outlines revenue models, buyback mechanics, and investment plans, while sharing his long-term vision of transforming RESI into a multi-protocol ecosystem that could one day rival traditional real estate finance.

AWS went down, so we kicked it old-school on Zoom with Seby (RESI, Subnet 46). Quick refresher: RESI is building a real-estate oracle—unlocking U.S. property data and on-chain intelligence for lenders, DeFi/fractional RE, and proptech. Seby walked through fresh updates: a new white paper and alphanomics, DSV added to their OTC stack, V2 moves from APIs to scraping (cheaper for validators), V3 splits the subnet into data, inference, and storage (with Hippius) so prompts like “price this home from its inspection” fetch comps, analyze reports, and persist results. A public dashboard is rolling out, and go-to-market leans on IDX-style white-label portals for brokerages to drive viral distribution.

On revenue, RESI’s already selling Predict CASA data packs via a paid funnel (Meta ads → two-call close) and tightening the machine with hires and higher spend; the Oracle appraisals target fractionalized/DeFi real estate at $500 setup + $100/mo. Alphanomics is pure-alpha (no new token): stake for pricing tiers, LP with alpha for deeper discounts, and a planned sidechain where gas/wrapped-alpha and LP incentives amplify buy pressure as builders launch on top. TL;DR—Resi’s shipping product, lining up customers, and scaling a sales engine to convert leads into buybacks while turning a locked market into composable, on-chain real-estate intelligence.

NEWS

Announcements

Resi Labs | Subnet 46 Follow 104 475

Real Estate Super Intelligence | Subnet 46 | The Real Estate Oracle

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