Builder Spotlight: Matthew A. Schneider — Building, Inc

👋 GM frens! Welcome to Builder Spotlight, where we talk to the people actually shipping the next wave of Web3.

In this series, we spoke with Matthew A. Schneider, CEO of Building, Inc — the team turning fragmented, offline property records into structured, finance-ready data so assets can be appraised accurately, audited cleanly, and tokenized credibly.

“You can tokenize almost anything. But if you skip data, pricing, and compliance, your tokens won’t sell — and they shouldn’t.” — Matthew A. Schneider

👤 Matthew A. Schneider – Building, Inc

Matthew spent his early career in capital markets—day trading equity index futures and options—before diving into real estate in 2020 with one big question: how can blockchains add efficiency and transparency to a notoriously opaque industry?

Very quickly, he ran into the same roadblocks everywhere:

• Mistrust & opacity: parties can’t see the same truth about a property.
• Low digitization: PDFs, paper trails, WhatsApp threads, and random Drives.
• Non-standard pricing: appraisals vary wildly; fair value is hard to defend.
• Compliance gaps: fragmented records make underwriting and audits slow and risky.

“You can tokenize almost anything. But if you skip the data, the appraisal, and compliance, your tokens won’t sell — and they shouldn’t.”

His first foray was a marketplace for tokenized real estate. Tokens were easy; buyers weren’t. That lesson sparked a hard pivot: focus on everything that happens before tokenization—gathering, verifying, and structuring property data so assets become finance-ready.

Research on this “data-before-tokens” stack began in early 2021, and the company incorporated in 2024 as Building, Inc. What started as a way to clean up messy property files has evolved into on-chain-anchored data rooms, machine-readable records, and digital twins that feed oracles—so appraisers, auditors, lenders, and tokenization platforms all work from the same living source of truth.

The goal? Collapse the bid–ask spread with defensible pricing and real-time performance data, making collateralized lending and secondary trading actually viable. It’s standard practice in traditional capital markets—price, provenance, risk—but still rare in Web3. Building’s mission is to make it the norm for real estate RWAs.

✅ Key Summary

• Data-before-tokens: Building focuses on the pre-tokenization stack — verify, structure, and standardize property data so tokens have defensible value.
• On-chain provenance: Every record is anchored with a cryptographic fingerprint, enabling audit-ready compliance and transparent ownership trails.
• Digital twins → oracles: 3D models + IoT streams convert buildings into living data sources, keeping price feeds and risk models fresh on-chain.
• Institution-grade workflows: Designed for appraisers, auditors, lenders, and insurers to collaborate from a single source of truth.
• Liquidity, the right way: Accurate appraisals and continuous data reduce the bid–ask spread, making secondary trades and collateralized lending more feasible.
• Where they’re active: North America first, with global pilots across Europe, the Middle East, Asia, and Africa.
• Who should partner: Asset owners/REITs, tokenization/STO platforms, and digital-twin/IoT providers.

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What pulled you into crypto and, specifically, real estate RWAs?

My crypto journey really began in 2020. I’ve always been interested in decentralized ledgers as a way to bring efficiency and transparency to old industries. Coming from day trading and then spending time in real estate, I saw the same problems everywhere: mistrust between parties, opaque projects, low digitization, contracts not respected. Blockchains solve those, at least in principle. So I asked: how do we apply that to real estate and construction?

What was the first thing you built, and what did it teach you?

A marketplace for tokenized real estate. We built a strong pipeline and actually began tokenizing properties. The big surprise was demand: getting people to buy those tokens. That’s when I made my biggest pivot. You can tokenize anything, but without data, appraisals, and compliance, tokens won’t clear markets. So I moved upstream and focused on everything before tokenization — gathering, verifying, and organizing data so that when you do tokenize, it works.

“Most teams skip Step 1 and jump to tokens. Then they wonder why there’s no trading volume.”

So… What Is Building ?

Think of us as the digitization and validation layer. We’re not the exchange; we’re the Bloomberg/Carfax for the asset before it hits markets. We:

1. Digitize scattered records into a living, audit-ready data room and anchor them on-chain;
2. Make documents machine-readable;
3. Connect or create digital twins so the asset’s performance and health are visible;
4. Enable accurate, defensible appraisals;
5. Then hand clean inputs to tokenization partners and oracle feeds.

Who are your users and what they hiring Building to do?

• Developers & asset managers: Organize messy docs, reduce underwriting time, prove compliance, maintain an always-current source of truth.
• Tokenization/STO platforms: Get credible inputs (data + pricing) so offerings are investable and auditable.
• Appraisers/auditors/lenders/insurers: Access verified documentation and digital twins to do their work faster and with more confidence.

“Tokenization doesn’t create liquidity” is a strong statement. Can you unpack it?

Liquidity happens when a buyer and seller agree. They agree when both sides trust the price and risk. That trust comes from data — historicals, market context, maintenance records, insurance, climate exposure, and a defensible appraisal. Tokens alone don’t supply any of that. Data is the precursor to liquidity.

Where does accurate pricing come from in your model?

Start with the data room: documentation, permits, maintenance, leases, insurance, environmental, capex/opex — all anchored on-chain with provenance. Make it machine-readable. Add a digital twin so you see geometry, components, sensors, and real-time performance. Now appraisers can generate better fair market value; oracles can stream those signals on-chain. That’s the feedback loop that keeps a token finance-ready.

What’s the toughest part of this work?

Fragmentation. Real estate is still analog: PDFs, paper, WhatsApp threads, random Google Drives, even filing cabinets. The hardest job is collecting, cleaning, and structuring it so stakeholders can trust it — and so AI can actually use it. Many teams see that difficulty and skip it; that’s why tokens later feel risky and don’t trade.

If I’m a portfolio owner, what does onboarding look like, step by step?

1. Kickoff & Scope: Pick assets; align on schemas/checklists.
2. Digitize: Upload records; we anchor provenance (who/what/when) on-chain.
3. Normalize: Convert into machine-readable formats; fix gaps.
4. Twin (ideal): Connect/create a digital twin with component metadata and sensors.
5. Appraise: Third parties can appraise with full access; lenders/auditors review in parallel.
6. Tokenize (via partners): Map equity/debt to contracts; oracles keep on-chain values current.

Where are you active today, and do you have to be on site?

We’re focused on North America first, but we have inbound interest from Europe, the Middle East, Asia, and Africa. We work remotely by design — our framework lets owners upload locally. Digital twins reduce the need for site visits because stakeholders can inspect the asset virtually with real-time telemetry.

You’ve mentioned ratings or certifications for tokenized properties. Why do we need those?

Not all tokenized offerings are equal. If we can certify data richness, compliance status, location signals, design/ops quality, and maintenance — plus deliver a defensible appraisal — then institutions can differentiate. Ratings compress the bid–ask spread and attract volume.

What are the most common misconceptions you hear in “RWA land”?

• “Fractionalization creates liquidity.” It doesn’t. Data and price do.
• “Listing is the use case.” It’s not. The use case is better financing, better underwriting, better risk — then listing.
• “AI will figure it out.” Not without machine-readable inputs. Garbage in, garbage out.

What does success look like for you over the next year?

Two tracks in parallel:

1. Better real estate: smarter operations with IoT, automated maintenance, and digital twins.
2. Better tokenized real estate: clean data rooms, oracles, and certification so assets can trade or be collateralized.
   We’ll expand pilots (US/EU/ME/Asia), deepen lender/insurer integrations, and collaborate closely with tokenization platforms.

Who should reach out to you after reading this?

• Asset owners, developers, REITs who want portfolios finance-ready.
• Tokenization/STO platforms missing the data/compliance layer.
• Appraisers, auditors, insurers ready to plug into verified data rooms.
• IoT & digital-twin providers to enrich property graphs and feeds.
  If that’s you — let’s launch a pilot.

TL;DR

• Thesis: Tokenization ≠ liquidity. Trustworthy data and credible pricing are the precursors to liquidity.
• What Building does: Centralizes and verifies property data; anchors documentation on-chain; powers accurate appraisals, compliance, digital twins, and oracle feeds.
• Who it’s for: Developers, asset managers, STO/tokenization platforms, and the institutions (lenders, appraisers, auditors, insurers) around them.
• Asks: Intros to portfolio owners and tokenization platforms to run digitization → appraisal → tokenization pilots globally.

Try It — Portfolio Onboarding in Practice

1. Start: Pick a property (or a dozen).
2. Upload: Dump every doc you have — we’ll structure it.
3. Anchor: Provenance is written; the source of truth is born.
4. Model: Add or connect a digital twin; stream real-time data.
5. Appraise/Audit: Invite third parties to diligence asynchronously.
6. Tokenize: Use your preferred partner; connect oracles to keep markets honest.

👉 Start here: https://www.building.inc/

What You Can Do

• Own or manage a portfolio? Run a digitize → appraise → tokenize pilot.
• Operate a tokenization platform? Plug in data/compliance to raise clearance.
• Part of the assurance stack? Appraisers, auditors, insurers — integrate with verified data rooms.
• Build digital twins/IoT? Partner on the property graph and real-time streams.

Intros welcome. If you’re working on the future of real assets or infrastructure-grade tech, Matthew wants to talk.

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