The Appraisal, June 2026 — Breaking down Entrata's S-1

This is a special edition of the newsletter — we’re breaking down Entrata’s recent S-1 filing. Their IPO will be an interesting case study on how public markets value vertical software businesses in the age of AI.

Founded in 2003 in Lehi, Utah, Entrata sells property management software to multifamily owners and operators. It powers 2.5M units (~10% of the U.S. multifamily market) and counts 4 of the NMHC top-10 operators as customers. Like its peers in the PMS space (Yardi, RealPage, Appfolio, etc.), Entrata bundles property accounting, leasing, payments, resident experience, and utility and resident billing into a single system of record.

After two decades in private hands it’s finally stepping into the light. The company is targeting $500M as part of its IPO and will trade on the NYSE under the ticker ENT. It’s likely the largest software company ever to IPO while growing 20%+ and achieving GAAP operating profitability. Let’s dig in!

Financial Profile

Here’s the headline financial picture from the filing:

• Revenue of $509M in 2025, up ~24% from $412M the year prior. Subscription-related revenue is ~86% of the total; embedded payments are the smaller but faster-accelerating piece, growing ~34% year-over-year in the most recent quarter.

• 2.49M billed units at an average revenue per unit (ARPU) of ~$216 per unit per year.

• Gross margin of 63% in Q1’26, up from 54% in Q1’24.

• GAAP operating margin of 16% in 2025, and 26% in Q1’26. This is a profitable software company; Entrata generated $51M of net income in 2025, up from $22M.

• Net revenue retention of 117% and gross retention of 97–99%. Customers generating more than $500K of ARR grew 27% to 233 accounts, and now represent 84% of total ARR.

This is a profitable, sticky, enterprise software business. Revenue compounds ~4% a quarter, margins are expanding, and net revenue retention is strong (all hallmarks of a classic control point business).

Entrata’s revenue comes in two forms. The bulk — about 86% — is subscription: recurring fees for the software platform and its modules (accounting, leasing, payments, resident experience, billing). The other ~14% is embedded payments — Entrata takes a cut of the rent and fees that flow through the platform, so that line scales with payment volume rather than seat count.

The two are growing at almost the same pace — subscription ~23%, embedded payments ~25%. That’s worth noting: Entrata isn’t leaning on payments to paper over a software slowdown. Both engines are running, with payments just edging ahead.

Revenue growth is driven by two levers: more units, and more revenue per unit. In 2025, billed units grew ~15% — Entrata won and expanded enterprise accounts — while ARPU rose ~8%, from $194 to $209. That’s roughly two-thirds volume, one-third price: a healthy composition, with real unit growth underneath rather than pure price hikes.

Over the most recent year, units grew ~12% while ARPU jumped ~10%, to $216 — and the split moved closer to 55/45. ARPU is becoming a bigger share of the story as the installed base matures and Entrata sells more modules and payments into it. That’s the “monetize the base harder” dynamic the bull case rests on, already showing up in the numbers — and worth watching to see if it keeps building. Entrata's ARPU is already ~2x AppFolio's, and likely best-in-class amongst its PMS peers. The question is whether it can keep growing revenue per unit by selling more products around the PMS core.

Net revenue retention sits at 117% in both 2024 and 2025, meaning every cohort spends ~17% more a year later. Gross retention is 97–99%. And customer concentration is improving in the right direction: customers over $500K of ARR grew from 183 to 233, and now make up 84% of revenue.

Entrata does ~$500M of revenue inside a serviceable market it pegs at ~$5B today, so its current ~10% market share implies there is room to capture incremental share over time. However, it’s an extremely crowded category: the PMS space is mature and fully contested, and the underlying unit base barely moves, growing just ~2–3% a year.

But two green shoots stand out:

• The first is consolidation — the 50 largest operators have grown their managed units ~48% since 2020 (an ~8% CAGR), concentrating share into exactly the enterprise segment Entrata serves best, so its real addressable market is compounding far faster than the housing stock.

• The second is demographics — renters are forming households roughly 2x faster than homeowners through 2040, and housing affordability challenges are keeping people renting longer. The result is a structurally growing, fuller base of rental units.

Entrata vs. AppFolio

We’ll soon have two publicly traded PMS vendors in the real estate index. AppFolio climbed up from SMB — it won the smaller operators first, then moved upmarket. Entrata is coming down from enterprise — it won the largest operators first, and is now pushing toward the middle. They’re converging on the same stretch of the market from opposite ends. Here’s how the two companies compare:

• Growth. Entrata is growing faster (24% vs. 20%) and has been for a few quarters running.

• Rule of 40. Dead even at ~47. Both healthy; neither in the elite 50–60 band on a full-year basis.

• Scale. AppFolio is roughly 2x the revenue (~$995M vs. $509M TTM) and sits on ~9.5M units under management against Entrata’s 2.49M billed units.

• ARPU. Entrata does a better job monetizing its customer base — $216 per unit per year (roughly 1% of gross rents) vs. AppFolio’s $103 per unit per year. Part of this is explained by Entrata’s focus on enterprise segments vs. AppFolio’s more SMB focus.

• Margins. Comparable in the latest quarter — Entrata’s ~28% non-GAAP operating margin edges AppFolio’s ~27%. But AppFolio’s full-year margin is higher and cleaner.

Valuation Expectations

In a pre-AI world, this is a slam-dunk IPO — the kind of clean, durable software name that likely trades at 10x+ forward revenue without anyone thinking twice. The same profile now draws a sharper question: is a two-decade-old system of record a fortress, or a target?

As discussed above, the closest public comp is AppFolio at ~5.4x revenue. Run Entrata through the same lens (similar growth, identical Rule of 40, comparable margins) and the comp set converges on roughly that same ~5.4x. That implies an enterprise value around $2.7B on trailing revenue, closer to $3.3B on a forward basis. For context, Blackstone invested in Entrata at a $4.3B valuation in May 2025.

Bull case → Entrata is a durable, profitable system-of-record that keeps getting more valuable over time — compounding at ~20%+ by layering payments, lending and other ancillary software products. It monetizes its existing install base harder (more revenue per unit) without having to win new logos to maintain growth.

Bear case → Entrata is one of several players competing for share in a crowded, competitive PMS category — up against entrenched incumbents like Yardi and RealPage, public peer AppFolio, and emerging AI upstarts. Its decelerating growth is an early signal of this trend. A wave of AI-native challengers will rebuild the leasing, billing, and operations stack as agent-first products rather than modules bolted onto a two-decade-old platform.

Where do I expect pricing to land? We can look to ServiceTitan’s public market performance as a guide (see my S-1 breakdown here). The company went public in December 2024 as the best vertical-SaaS story in years — popping 42% in its IPO to close at $101, and ran to ~$129 by May 2025, valuing the company at well north of 10x revenue. Since then, ServiceTitan’s share price has been cut roughly in half. It trades at around $64 today, closer to 5–6x revenue — and that’s despite growing ~24% the entire time. The business kept executing, but the multiple compressed anyway, as SaaSpocalypse fears scared off software investors in an AI world.

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Thanks for reading! As always, please feel free to share feedback or thoughts on this newsletter — you can find me on LinkedIn and Substack. If you have any colleagues that may find The Appraisal valuable, please share this newsletter.

Note: Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others.

The Appraisal, April 2026 — The State of Data Centers in 10 Charts

This is a special edition of the newsletter on the data center development boom. Data centers are, at their core, a real estate story. They require land, permitting, construction, and long-term leases — and the markets where they concentrate tend to see ripple effects across industrial, office, and energy infrastructure. Let’s dig in!

AI is driving one of the biggest infrastructure build-outs in modern history. Meta, Alphabet, and Amazon are now collectively funneling more than a third of their revenue into capital expenditures, driving data center development to more than 1.2% of U.S. GDP. Whether this is rational catch-up to surging demand or the early signs of an infrastructure bubble — think 1990s telecom or 1870s railroads — is a hotly debated topic. Here are 10 charts that help make sense of it.

1. Historic capital deployment into AI infrastructure

Big Tech is pouring capital into data center infrastructure at a pace with no modern precedent. As a percentage of total economic output, this spending exceeds the peak of the 1990s telecom boom. And for the first time, data center construction spending has nearly eclipsed general office construction — $42.1B vs. $40.5B annually.

The capacity pipeline tells a similar story. In 2010, the U.S. had less than 1 GW of total data center capacity. It took the better part of a decade to reach 4 GW. Then the curve bent — 5.5 GW in 2020, 9.2 GW in 2022, 11 GW in 2023. By 2025, there are 34 GW under construction and 88 GW in the planned pipeline.

Real private fixed investment in data centers has nearly tripled since 2020, while every other category of non-residential construction has barely moved — indexed to Q1 2020, data centers are at 295; everything else is at 98. The two lines, which tracked each other for years, have completely decoupled.

2. Demand for compute is accelerating

The obvious question is whether anyone actually needs all of this. So far, the answer keeps coming back yes.

AI is monetizing faster than any prior technology wave. Cloud revenues across AWS, Azure, and Google Cloud accelerated in 2025. Aggregate revenue is approaching $100 billion per quarter and is expected to continue growing at roughly 26–30% through 2026 — with no meaningful deceleration.

The major AI labs are reporting remarkable revenue growth — Anthropic announced that it had surpassed $30 billion in annualized revenue this month, up from $19 billion the month before and from $1 billion just fifteen months earlier. OpenAI is on pace to surpass $25 billion in revenue this year.

Usage data tells the same story. Token consumption on OpenRouter hit 7.6 trillion per week in Q4 2025. GitHub hit 1 billion commits in all of 2025; it’s now running at 275 million per week, on pace for 14 billion this year if growth holds.

At the peak of the dot-com bust, most of the fiber that had been laid sat completely unused — so-called 'dark fiber' that wouldn't see real demand for another decade. Today's GPU utilization tells the opposite story. AI data centers are running at 80% capacity, and operators are struggling to bring supply online fast enough to keep up. The hyperscalers are consistently reporting that AI workloads are outpacing available capacity.

3. Power is the bottleneck

Despite historic capital deployment and demand that shows no signs of plateauing, a hard constraint is emerging — and it has nothing to do with chips or capital. Getting power to a data center has become the defining bottleneck of the AI infrastructure build-out.

New data center project announcements dropped nearly 50% last quarter — not because demand dried up, but because securing a power connection has become nearly impossible. Developers aren’t starting new projects; they’re focused on finishing the ones already in the queue. The bottleneck has moved from “can we fund it” to “can we plug it in.”

A survey of 149 senior data center professionals lays it out plainly. 92% cite utility or transmission access as their primary constraint. Permitting is second at 86%. Fiber availability third at 85%. The list reads less like a construction industry survey and more like a report on failing infrastructure.

To work around it, 62% are building on-site power generation — fuel cells, turbines, solar — essentially becoming their own utilities. Another 40% are relocating projects entirely to wherever the grid has room.

The downstream implications are significant. Data centers consumed 3.7% of total U.S. power demand in 2023. McKinsey forecasts that figure hitting 11.7% by 2030. To put that in context: the entire U.S. residential lighting load is roughly 5%. In seven years, AI infrastructure alone could draw more than twice that.

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Thanks for reading! If you have any colleagues that may find The Appraisal valuable, please share this newsletter.

Note: Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others.

We Surveyed 150 Real Estate Professionals on AI Adoption. Here's What We Learned.

I’m excited to release the inaugural State of AI Adoption in Real Estate report. You can download the full report here, and I’ve included my highlights below.

The real estate industry has caught AI fever. Over the past year, the universe of founders building AI tools for real estate has exploded—spanning acquisitions, property management, leasing, underwriting, and everything in between. Last year I published my list of the top 50 companies at the intersection of AI and real estate. That list could easily be twice as long today.

But how much of this is hype, and how much is actually landing with operators? To find out, we partnered with our friends at Keyway to survey 150 real estate professionals across firm sizes and functional roles. The goal was simple: understand the true state of AI adoption—what’s being deployed, what’s being spent, and what’s getting in the way. The findings are now available in our first State of AI Adoption in Real Estate report.

You can download the full report on Keyway’s website, and I’ve included my key takeaways from the survey below.

1. The Pilot Paradox

The real estate industry has embraced AI experimentation with remarkable enthusiasm. Forty-five percent of firms are actively running pilot programs, and another 26% are in research or planning phases. Only 2% report no AI initiatives whatsoever.

Yet this activity has not translated into scaled deployment. Just 9% of firms have achieved enterprise-wide AI implementation. Another 18% have reached departmental deployments, leaving the remaining 73% either testing, planning, or stalled.

The implications are significant. Nearly half the industry is investing resources in pilot programs. The pilot-to-production bottleneck appears to stem from multiple factors: inadequate data infrastructure, unclear success metrics, and organizational resistance to moving beyond controlled experiments.

The root cause may be architectural. Investor Jamin Ball argued that assistive AI—tools designed to support rather than act—hits a natural ceiling:

Jamin Ball recently published a good post on why so many pilots stall:

An assistive system still depends on human attention to move forward. And human attention is exactly the scarce resource companies are trying to escape.

This is why so many AI initiatives feel stuck in a strange middle ground. They are clearly useful. They are often loved by users. And yet they fail to produce the kind of economic impact that was promised. The problem is not that the AI is bad. The problem is that it has no authority.

Real estate firms may need to decide: keep AI in an advisory role and accept the “attention tax” or grant it the authority to act and redesign workflows around that shift.

2. The Trust Barrier

While most real estate operators are optimistic about AI’s potential in the sector, many remain skeptical of the reliability of today’s models. Nearly half of respondents report that their firms distrust AI-generated financial analysis. Only 11% describe fully trusting AI outputs.

This trust deficit creates a ceiling on AI’s potential applications. While firms can deploy AI for back-office efficiency gains, many of the highest-value use cases—underwriting support, market analysis, and deal evaluation—require a level of firm-wide trust that currently doesn’t seem to exist at many organizations.

Harvey co-founder Winston Weinberg recently described how the company cleared early trust hurdles by a) augmenting LLM outputs with detailed sources and b) keeping lawyers firmly in control:

So one of the largest advantages that we had in the beginning was how much emphasis our product had on citations. It was very important in the legal industry to be able to cite, like, line by line and [ensure] the accuracy of those citations.

[Lawyers] are actually in the loop at all times. When you’re selling to an in-house team, it is better to sell them the specialized versions of tasks that they can kind of see an insanely high accuracy level on. And the more specialized you’re building, the higher accuracy you can get because it’s easier to fine tune, it’s easier to do evaluation because there are less steps, and the surface area is just smaller.

Real estate AI products may need to follow a similar path: build narrow, specialized tools like lease abstraction or comp analysis where accuracy can be demonstrably high, and keep professionals in the loop at every step. Trust compounds from there.

3. Bullish on AI, Bearish on Layoffs

Real estate professionals are genuinely optimistic about AI. Nearly 80% believe it will benefit their business over the next five years.

What’s notable is what respondents don’t expect: job losses. Only 18% believe AI will reduce team sizes, while 57% actively disagree. And behavior tracks with belief—91% of firms are using AI to boost efficiency, but just 18% plan any headcount reductions. Among the firms that have already deployed AI enterprise-wide, not a single one is planning cuts. The prevailing view is that AI makes teams more productive, not smaller. Real estate has landed on an augmentation model, treating AI as a tool that amplifies human judgment rather than a technology that replaces it.

4. AI Spend Is Rising

More than half of respondents plan budget increases more than 20% for AI initiatives over the next two years. An additional 22% plan increases in the 11-20% range. Only 12% anticipate flat or declining AI budgets.

These are substantial commitments that will reshape technology spending across the industry. For a mid-size firm with a $2 million technology budget, a 20% AI allocation represents $400,000 in annual investment—funds that must be sourced from existing operations.

To fund these increases, firms are primarily targeting reductions in outsourcing (43%) and administrative costs (41%), rather than relying on headcount reductions (18%). Firms are betting that AI will generate sufficient productivity gains to fund its own expansion without requiring staff reductions.

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The full report includes additional data on adoption by firm size, asset class, and role, along with detailed breakdowns of where legacy vendors are winning and where new entrants are gaining ground. Download it HERE.

As always, please feel free to share feedback or thoughts on this newsletter — you can find me on LinkedIn and Substack.

Note: Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others.

Figure S-1 Breakdown — The HELOC Machine on Crypto Rails

This is a special edition of the newsletter — we’re breaking down Figure’s S-1. It showcases a tech-enabled real-estate lender at real scale and profitability—with a clear, practical crypto layer. Let’s dig in!

Figure filed its S-1 last week. Figure is a blockchain-powered capital marketplace that connects origination, funding, and secondary market activity. This was one of the first investments I worked on at Thomvest way back in 2018. Our first meeting with co-founders Mike Cagney and June Ou took place in their living room, which doubled as Figure’s HQ in the early days. The company has come a long way since then, but it’s remarkable how much of Mike and June’s original vision remains intact 7+ years later.

Figure’s core business today centers around its mortgage technology efforts: the company originates and sells standardized HELOCs (home equity line of credit), increasingly for third-party brands, then take fees across origination, sale, and servicing. Crypto appears in its plumbing (blockchain records, lien/eNote registry) and in its longer-term vision (a digital asset exchange and a tokenized stablecoin/security), but near-term economics are tied to housing credit, not tokens. For public investors, that makes the model more legible—and testable—as a tech-enabled originator, with added upside from its blockchain platform.

The company has reached impressive scale — over the last year, Figure has facilitated more than $6 billion of home equity lending. From the start, Mike and June built the business around two key tailwinds:

1. There is massive, largely untapped demand for HELOCs — U.S. homeowners sit on roughly $11T of “tappable” equity (about $203k per borrower) yet more than half of mortgages carry rates at 4% or lower, making cash-out refinancing unattractive. A second-lien HELOC unlocks cash without disturbing the cheap first lien. HELOCs fund two important use cases—debt consolidation (there is more than $5T in outstanding consumer debt) and home improvement (the average home is more than 41 years old)—at lower cost than unsecured personal loans and credit cards.

2. The $3T global securitization industry is full of high-rent third parties, which extract 75–100 bps of fees in a typical securitization ($30B+ in total fees annual). Blockchains have emerged as a viable way to transform the loan lifecycle into a shared, low-cost system of record that cuts friction and errors—while promising tighter spreads and better secondary liquidity. The result is not “crypto for crypto’s sake,” but a fee-compression and liquidity story that aligns with how capital markets actually clear.

But why build both under one roof? The combination of the two businesses is powerful — Figure’s lending business acts as a powerful first customer and proof-of-concept for the blockchain-based securitization platform. It’s meaningful in banking, where regulation and general risk-aversion makes “first-mover” behavior uncommon. From the S-1:

“We made this pitch to the banks, and they universally said, “This is great! We love it! We’d like to be the 10th bank to do this…” It was clear this wasn’t an ‘if you build it, they will come’ situation – if we just built it, no one was going to show up.”

I’d also argue that the combination of origination and capital markets automation is key to actually delivering on speed and cost improvements in the mortgage value chain. The Figure team recently published a good piece on the topic, underscoring the cost savings enabled by their approach:

“Recording loan attributes immutably at origination reduces audit costs by 80% compared to legacy systems that require a full asset review with every ownership transfer.”

Let’s dig into these two segments, how they are connected, and what the the company is building to expand its product portfolio.

Figure’s Origination Business

Figure has built a modern HELOC stack, including proprietary loan origination system (LOS), automated verification of income and title, and efficient secondary market execution (more on that in the next section). The operating payoff shows up in two places that matter: speed and cost. Median time from application to funding is 10 days versus a 42-day industry median, and average production cost per loan is ~$730 versus an MBA-reported mortgage average of ~$11,230 in Q4 2024 (note that this is for all loan types, not solely HELOC).

In 2024, total net revenue rose 62.7% to $340.9M, and the company swung to $17.2M of net income from a $47.9M loss in 2023—driven by higher gain-on-sale, origination, and servicing economics. Momentum has carried into 1H25: net revenue reached $190.6M, up ~22% year-over-year, with net income of $29.1M and adjusted EBITDA of $47.9M.

Scale increasingly comes via partners: 168 as of June 2025; the top 10 partners drove 57% of origination volume in 1H25. Partner-branded originations grew from $149M (June 2022) to ~$4.3B over the last twelve months (as of June 2025), and were 77% of total originations in 1H25. Figure-branded volume (originated via DTC channels) reached ~$2.0B LTM (as of June).

A typical Figure HELOC partner is a medium-to-large U.S. non-bank mortgage finance company (though banks, credit unions, servicers, and wholesale brokers also participate) that wants a turnkey HELOC without building the stack. They run a white-labeled, partner-branded portal on Figure’s LOS—automated underwriting, decisioning, and closing under the partner’s name. Partners contract on a non-exclusive, auto-renewing basis to use the LOS and the Figure Connect marketplace, which is monetized via percentage and volume-based tech and execution fees.

Figure’s top line comes from (i) ecosystem & technology fees (LOS and Connect; plus program fees for securitizations), (ii) origination fees, (iii) gain on sale, (iv) servicing fees, (v) interest income, and (vi) changes in servicing asset fair value. Partner contracts pay volume-based technology/processing fees tied to principal balances, while program fees are earned on securitization close. In practice, this means the platform monetizes both the creation of loans and the liquidity events that follow.

In 2024, gain-on-sale rose 179% to $140.4M as pricing improved ~110 bps and unpaid principal sold increased from $3.3B to $4.8B; retained servicing asset gains also expanded with larger sale volume and slower prepay assumptions. Servicing fees climbed with the servicing book (UPB $8.1B vs. $5.2B). Technology offering fees scaled as partners used the LOS (weighted-average ~0.8% of principal in 2024). Nearly all originations are HELOCs (99% in 1H25), so category growth maps to that product’s throughput.

Figure’s revenue mix is moving toward platform fees. Ecosystem & technology fees were $28.3M in 2024 (~8% of net revenue), then $43.8M in 1H25 (~23% of net revenue). That’s a meaningful step-up in recurring, non-spread income while core credit throughput stays high.

Figure’s Blockchain Platform

While the near-term revenue engine is HELOC origination and sale, Figure’s blockchain efforts are an important efficiency driver for the business. When combined, they allow Figure (and its originator partners) to prove ownership and servicing rights on-chain, match loans to buyers, and eventually finance and trade them against a native, interest-bearing stablecoin. From the S-1:

“Financial services historically have been and still are trust-based markets. Trust-based markets require significant intermediation. Up to seven parties sit in between a buyer and seller of public equity. Five parties intermediate a debit card transaction. Massive companies with enormous market capitalizations have been built around this rent-seeking. Blockchain has the power to distill these multi-party marketplaces down to just two: buyer and seller. All the rent-seeking goes away.”

The S-1 shows early adoption where it matters (registries and loan distribution), modest traction in trading, and seed-stage scale in yield products. Let’s walk through their product portfolio:

Provenance (public blockchain). Provenance is a public, proof of stake decentralized blockchain (Figure owns 20% of the network’s utility token, $HASH). Provenance is the “system of record” for assets on the Figure platform, i.e. a public Layer-1 used to hash each origination and update ownership as loans trade. Figure pays gas fees in HASH and currently subsidizes participant fees; average gas consumption is <1 HASH per transaction (~$0.026). The chain’s performance (≈4.4s blocks) and low fees make it viable for back-office scale. Figure cites >$50B in cumulative on-chain transaction records (as of June 2025), ~$10B of “real world assets” (RWA) value on-chain (TVL), and ~75% share of tokenized private credit by outstanding loans (RWA.xyz).

Provenance launched in 2018 as Figure’s big move into the blockchain ecosystem. Today, every asset passing through Figure’s origination system is recorded on Provenance, and many other buyers / seller pairs utilize the blockchain’s smart contract framework (here is a good case study on how JP Morgan utilizes Provenance).

DART (on-chain lien & eNote registry). Digital Asset Registry Technologies (DART) is an electronic registry that records lien/eNote control and updates ownership automatically as loans are pledged or sold. It runs on Provenance and sits alongside county records. Adoption moved fast—about 80% of loans originated on Figure’s LOS were boarded to DART in 1H25 (up from ~2% in FY24), with ~30k loans relying on DART and ~75 entities signed to participation agreements by June 30, 2025. The pay-off is fewer manual assignments, cleaner pledging/sale, and lower post-closing friction.

Figure positions DART as a real-time, programmable successor to MERS, which is the primary recording system for tracking mortgage ownership and servicing rights in the U.S. Instead of updating ownership/control through manual assignments and disparate systems (as is the state of the art for MERS), DART “listens” to on-chain events and synchronizes control automatically, while preserving county recording and traditional documentation.

Figure Connect (loan marketplace). A standardized, electronic market for HELOCs where assets originated on the LOS can be sold to whole-loan buyers or prepped for securitization; Provenance is the system of record for asset transfers. In its first 12 months (to June 2025), Connect handled ~$1.3B of HELOC flow with ~27 participants and generated ~$45M of net revenue in 1H25 (from de minimis in 2024). The model is volume-based tech/execution fees; disclosures indicate originators have historically netted >1% more UPB by selling on Connect than via Figure as an intermediary. A Sixth Street–backed guarantor vehicle exists to deepen the bid, though as of June 2025 its purchases via Connect had not yet begun—another execution milestone to watch.

Figure Exchange + ATS (tokenized RWA trading). A regulated exchange stack (broker-dealer/ATS + money transmitter/VASP registrations) for digitally native securities and tokenized real-world assets. Activity has crossed $1B cumulatively since launch, but 1H25 revenue was effectively $0—useful plumbing, not yet a P&L driver.

YLDS (yielding stablecoin). YLDS is an SEC-registered, interest-bearing stablecoin backed by cash and cash equivalents, paying a constant rate of SOFR minus 50 bps. It launched in Feb-25 and stood at ~$4M outstanding by Jun-30, 2025, contributing <$1k of revenue in 1H25. The S-1 positions YLDS as settlement collateral for Exchange and as a funding rail for Connect. The aim is to migrate more marketplace settlement into YLDS over time.

Democratized Prime (on-chain funding market). This is a Dutch-auction lending market where institutions and retail provide cash to specific collateral pools (e.g., tokenized HELOCs, margin funding). Auctions reset hourly; Q2-25 clearing rates ranged from 1% to 30% (weighted avg. ~10%). As of Jun-30, 2025, assets on the platform were ~$1.5M and fee economics are 50 bps (borrower-paid), with revenue to be recognized under ecosystem/tech fees once third-party funding scales. A planned cross-collateralization feature is slated for late-2025.

Closing Thoughts

The near-term economic engine is HELOC origination and sale, Figure’s crypto products act as core infrastructure. This underscores the strategy: build durable, fee-generating infrastructure around HELOC flow first; let adjacent blockchain products add optionality without driving the P&L near term (perhaps Amazon’s strategy building and ultimately monetizing AWS is an appropriate analogy). The timing is good given regulatory tailwinds around crypto and token securitization.

The company is already expanding its securitization efforts beyond HELOC. Figure CEO Michael Tannenbaum recently shared that the company had completed a securitization comprising of all first-lien mortgages:

“What made this securitization even more notable was that these loans could have gone to Fannie Mae or Freddie Mac, but instead went to Figure's marketplace. Our partners either explicitly (as is the case with most financial institution partners) or implicitly (because our marketplace was the best for pricing and liquidity) chose our market.”

Our use of blockchain and the related liquidity and transactional benefits we have brought to our marketplace has enabled us to catch up and beat what has been well known to be the most liquid and robust debt market.

Blockchain matters where it reduces funding frictions: (1) DART de-risks collateral and simplifies due diligence; (2) Connect increases take-out velocity and price transparency; (3) Exchange/ATS and YLDS create always-on settlement and, over time, balance-sheet-light financing; and (4) Democratized Prime adds market-clearing funding that can compress spreads. That stack is already showing up in mix: in 1H25, 22% of revenue came from transactions via Figure Connect, with the remainder split across origination, servicing, and interest income.

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Congratulations to the Figure team! As always, please feel free to share feedback or thoughts on this newsletter — you can find me on LinkedIn and Substack.

Note: Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others.

The Appraisal, August 2025 — Opendoor's Open Questions

Welcome to the August edition of The Appraisal, a newsletter on real estate tech. My writing focuses on the 30+ public companies operating at the intersection of real estate and technology. In this issue, we review Opendoor’s journey as a public company amid its recent share price run-up and strategic changes to its core iBuying model.

Opendoor has been the subject of intense social media discussion over the last quarter, including a popular post on the r/WallStreetBets subreddit, and viral tweets from hedge fund manger Eric Jackson (who correctly called Carvana’s big rally, suggesting that Opendoor had similar 100x potential). This led to a flurry of trading activity that drove the company’s share price up 5x in a matter of days (from $0.51 to as high as $4.97). Shares have since settled back to the $2 range.

I last wrote about Opendoor in October 2020, ahead of its SPAC led by investor Chamath Palihapitiya. The recent run-up, along with last week’s Q2 earnings announcement, inspired me to revisit that post and update the bull and bear cases for Opendoor. Let’s dig in.

Opendoor announced its SPAC during a period of intense investor demand for technology IPOs, particularly those within the real estate segment that were benefiting from fiscal stimulus and low interest rates. The transaction valued Opendoor at an enterprise value of $4.8 billion, representing a nearly 18x EV-to-Forward Adjusted Gross Profit multiple (adjusted gross profit strips out inventory valuation adjustments from GAAP GP).

Since then, the company and it’s stock have been on a rollercoaster. Opendoor grew aggressively in 2021 during a period of record-high home price appreciation. The company went on a home buying spree that year, purchasing more than 37,000 homes across 30+ markets. And rising home prices meant profitable home sales for the company — Opendoor generated more than $500 million in contribution profit in 2021 (profit on homes sold net of direct selling costs). Opendoor’s share price peaked at $35.88 in February 2021, representing a market cap of more than $16 billion.

Unfortunately, the good times were short-lived: beginning in early 2022, the Federal Reserve initiated aggressive rate hikes to combat rising inflation (ending the year at a Fed funds rate of 4.50%). In response to the rate hikes, Opendoor meaningfully slowed down its pace of home buying (from 14k in Q2 ‘22 to less than 4k in Q4 ‘22).

Rising rates also cooled buyer demand for homes, which impacted the company’s ability to sell its inventory of homes at a profit. Contribution margin went negative in Q3 ‘22, and by Q1 ‘23, the company lost nearly $30k per home sold (selling homes for on average 7.7% less than what they purchased them for, net of selling and holding costs). Opendoor’s share price fell below its SPAC price of $10 per share, and has not recovered since.

The Wheeler Era

By Q4 ‘22, Opendoor’s price per share fell below $2. And by December, the company’s founder and CEO Eric Wu announced he was stepping down. CFO Carrie Wheeler moved into the CEO role with a mandate to cut costs while navigating a challenging housing market.

Wheeler has led a period of aggressive cost-cutting. The company laid off more than 500 employees in 2023. Last year, Opendoor announced another workforce reduction of 300 employees (17% of its workforce). The company also spun off its transaction platform for institutional investors, Mainstay, in order to further reduce operating expenses (the company reported $35 million in annual savings resulting from the divestiture).

Adjusted operating expenses (which strip out stock-based compensation and variable costs related to selling properties) were $46 million in Q2 ‘25, down by over 50% from the same quarter last year.

Source: SFR Analytics

Opendoor has also improved the gross margins generated from its home buying efforts in recent quarters. The team at SFR Analytics put together a great chart in their recent piece on Opendoor, highlighting per-home gross margins generated by cohort. Recent cohorts are producing strong average gross profits. From their post:

Opendoor isn’t nearly as bad at acquiring properties as some people make the company out to be. In 2022, Opendoor was — but since then the company has been consistently gross margin profitable. In Phoenix, which accounted for >50% of total losses in 2022, the last 12 months have routinely been between $20k-$30k gross margin profitable. As the company has laid off the risk in 2025 acquisitions, margins have also gotten stronger.

However, those efforts have yet to generate durable earnings results, nor appease public market investors. The stock has consistently traded below its SPAC price for the last thee years. Just two months ago, Opendoor received a warning from Nasdaq that it could be delisted after the stock price failed to break above $1 for 30 consecutive days. In June, the company announced a special meeting to discuss a reverse stock split “ranging from any whole number between 1‑for‑10 and 1‑for‑50.”

While the recent retail frenzy over the stock served as a lifeboat for the company, that ship may now be sailing away. The company’s stock price slipped below $2 again following last week’s earnings announcement, in which management guided to weak Q3 earnings (only 1,200 homes acquired, projected revenue down 44% quarter-over-quarter to $875 million). Prominent investors have lobbied for broad leadership changes at the company.

Looking Forward

The iBuying model has several structural challenges — forcing operators to constantly tradeoff growth for margin. Macroeconomic volatility makes the job even harder. For example:

• Opendoor must buy homes at a meaningful discount to cover marketing, holding, selling, renovation, and overhead costs. As discounts deepen, seller conversion rates fall, pushing CAC higher and reducing purchase volume. According to a recent Opendoor investor presentation, “sellers are about twice as likely to sell their home at a 6% fee vs. a 12% fee.”

• Opendoor must turn inventory quickly to recycle capital and keep carrying costs low—especially in a flat or declining market—but that speed often means accepting lower gross margins. This may prove challenging in today’s market — home prices in cities like Dallas and Phoenix are down slightly year-over-year, while total inventory of homes for sale is up more than 15% (indicating a buyer’s market).

• Opendoor must be very responsive to changing market conditions that can impact buyer demand. But when it scales back home buying its fixed costs are spread over fewer homes, impacting the company’s ability to reach EBITDA profitability. This is playing out now — Opendoor is guiding to just 1,200 purchases for Q3 ’25 (versus 3,504 a year earlier) in response to headwinds in the housing market.

Let’s use some simple math to better illustrate the hurdle the Opendoor team must overcome in order to profitably manage this business:

• The company has averaged about $16.5k of contribution profit per home sold over the last 8 quarters

• Advertising costs per home sold average about $5.5k over the last 8 quarters (based estimates from UBS)

• So net contribution profit (stripping out advertising expense) is about $11k.

• Adjusted R&D and G&A expense in Q2 ‘25 was $38 million according to UBS (this is down by over half from 2 years ago) and is expected to stay within this range through 2026.

• In order to “pay for” these fixed operating costs, Opendoor must sell about 3,500 homes per quarter. Anything less implies that the company won’t produce contribution profits in excess of its costs. The company acquired only 1,757 homes in Q2, suggesting a reversion to negative EBTIDA margins in future quarters.

So what should Opendoor’s leadership do? Wheeler has described the company as a “leaner, more efficient operation” in 2025. I already highlighted the actions the company has undertaken to drive down operating costs. Now let’s discuss the company’s efficiency initiatives, namely Cash Plus and Key Agent.

Cash Plus provides sellers with upfront cash for their home, then professionally renovates and resells it on MLS. If the renovated home sells for more than Opendoor’s costs, the owner receives the additional proceeds (net of all fees and expenses incurred). This model combines the speed and certainty of an instant offer with the price-maximization potential of a traditional listing. Cash Plus is live in Dallas, Nashville and Raleigh and slated for wider rollout this year.

Key Agent flips Opendoor’s old direct-to-consumer model: the company now funnels high-intent seller leads and instant-offer tools to a hand-picked network of top agents, who present every option (cash offer, Cash Plus, or a standard listing) and earn a referral fee no matter which path the client chooses.

Both programs chip away at the two structural weaknesses that have plagued the iBuyer model — balance-sheet exposure and high customer acquisition costs. They achieve this by partnering with agents, as described by Wheeler in the company’s most recent shareholder letter:

“We believe a great agent can be an invaluable guide through one of life’s biggest decisions, and our path forward lies in deepening these partnerships.”

This is an important shift from prior messaging that tended to eschew the agent’s role in a real estate transaction. Opendoor is no longer trying to replace real-estate agents with its own end-to-end service; instead, it’s providing agents with ready-made tools— cash offers, renovation teams, pricing and market data—that they can plug into their own listing workflow.

Together, these initiatives reposition Opendoor as a capital-light marketplace that (hopefully) improve unit economics. Wheeler hinted at this in the shareholder letter, sharing some early data on seller conversion rates when agents are leading the conversation:

We began piloting this new approach in select markets in the second quarter, and the early signals have been encouraging:

Twice as many customers are reaching a final underwritten cash offer as compared to our traditional direct-to-consumer flow.

Listing agreement conversion rates (i.e., consumers who end up listing with a partner agent) are 5x greater than what we have realized in our direct-to-consumer flow.

These are good (early) signs of more sustainable unit economics. We’ll certainly learn more over future quarters.

Closing Thoughts

As I highlighted above, the central questions for Opendoor’s core iBuying business are two-fold: first, what is the contribution margin-optimal volume of home purchases per quarter (i.e. factoring the relationship between marketing spend and discount rate on homes purchased)? And second, can management drive down operating expenses to a level such that the company can comfortably generate contribution profits in excess of those expenses? This is a challenging task, as I highlighted in my original post:

While the core vision of the company is quite elegant (“building the digital one-stop shop to move” according to Opendoor founder Eric Wu), the path to realizing that vision is incredibly complex. It requires thousands of employees across dozens of markets, holding billions in home value at any given time.

That’s why the company’s efforts around Cash Plus and Key Connections are so critical — they offer the promise of a more sustainable business that can better navigate changing housing market conditions.

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Five Trending Topics

1. The Housing Market Is Frozen. Zillow’s C.E.O. Knows You’re Still Scrolling [New York Times] Zillow is growing even as the housing market stays frozen—prices remain high, many owners are rate-locked, and home sales have hovered near 30-year lows. The piece attributes Zillow’s resilience to its platform scale and expansion into rentals, mortgage, and services.

2. Monopoly Round-Up: The $100 Billion Race to Become the Home Buying Super App [BIG Newsletter] Matt Stoller argues Rocket, Zillow, and Compass are racing to build a “home-buying super app” to capture the ~$100B in commissions now paid to agents, setting up a fight for market power in residential real estate. He outlines how private lawsuits and a pullback in federal antitrust enforcement could dismantle the old agent-centric model—or just swap it for new platform monopolies.

3. How Many Jobs Do Robots Really Replace? [MIT News] From 1990–2007, adding one robot per 1,000 workers cut the U.S. employment-to-population ratio by ~0.2 points—about 3.3 jobs per robot—and reduced wages ~0.4%. The effects are concentrated in robot-heavy regions and industries (e.g., autos) but create interesting questions as robots gain more widespread adoption, including across real estate and construction.

4. Measuring AI Ability to Complete Long Tasks [METR] METR proposes a simple metric—how long a task (in human-hours) an AI can complete—and finds this “time-horizon” has doubled roughly every seven months over the past six years. Today’s best models reliably handle tasks lasting minutes and falter on multi-hour work; if the trend holds, week- to month-long autonomous projects could arrive within a few years.

5. 1,000 Homes of Power in a Filing Cabinet — Rising Power Density Disrupts AI Infrastructure [Goldman Sachs] Goldman argues AI’s performance gains from tightly clustered GPUs are driving rack power from ~20–40 kW to ~600 kW by 2027 (with 1 MW on the roadmap), forcing a shift to liquid-cooled, industrial-grade data centers. It frames 2025 as a decisive buildout moment where obsolescence risk and export-control limits on interconnects will shape winners in the AI infrastructure race.

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Note: Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others.