RWA First Stock's Major Acquisition: Why Buy a 'Traditional' Mortgage Company?

Foresight NewsPublished on 2026-06-12Last updated on 2026-06-12

Abstract

On June 10th, Figure Technology Solutions (Nasdaq: FIGR), a blockchain-native capital markets firm, announced a $717 million acquisition of Kiavi, a leading non-bank lender for residential real estate investors. The deal involves Figure acquiring Kiavi's technology and operations for approximately $538 million, while forming a joint venture with alternative asset manager Sixth Street to purchase Kiavi's existing loan portfolio. Sixth Street also provided a $3 billion forward purchase commitment. This acquisition marks a strategic shift for Figure, known as the "RWA (Real World Asset) first stock," allowing it to expand significantly into the larger market of first-lien mortgages. Kiavi specializes in non-qualified mortgage (Non-QM) loans, such as short-term fix-and-flip (RTL) and rental property (DSCR) loans—a segment traditionally underserved by major banks. The move is expected to increase Figure's first-lien loan origination to over $7 billion annually, aiming for these to constitute about 40% of its business by 2027. Both companies leverage AI for underwriting: Kiavi uses proprietary models to value renovated properties and automate document processing, dominating the fix-and-flip lending space. Figure plans to integrate these assets onto its blockchain platform, Provenance, using its new 'Adaptor' product to standardize and tokenize the loans for institutional investors on its Democratized Prime marketplace. While the integration poses challenges—including merging dif...


Author: Sanqing, Foresight News


On June 10th, blockchain-native capital markets company Figure Technology Solutions (Nasdaq: FIGR) announced the acquisition of Kiavi for $717 million. Kiavi is a non-bank residential real estate investor lending platform founded in 2013, with cumulative loan origination exceeding $30 billion.


In the deal, Figure acquires Kiavi's technology and operational platform, funding approximately $538 million through the issuance of about $600 million in senior unsecured notes. The remaining roughly $179 million is contributed by global alternative asset manager Sixth Street, with the two parties forming a joint venture to purchase Kiavi's existing loan portfolio on its balance sheet. Sixth Street also provides a $3 billion forward purchase commitment. The two companies have been long-term partners. Kiavi's current CEO, Arvind Mohan, will join Figure as Chief Business Officer after the deal closes, leading the business integration.


Image source: Figure tweet


After the announcement, FIGR's stock price opened higher, briefly touching $30.11 during the session, but later retreated, closing at $28.07, down 0.74%, with an intraday fluctuation of about 9%.


Kiavi


Kiavi's predecessor was LendingHome, founded in San Francisco in 2013 by Matt Humphrey and James Herbert, with backing from investors like Foundation Capital, Ribbit Capital, and Renren.com.


It primarily offers two types of products: short-term transition loans (Residential Transition Loan, RTL) for property renovation investors, and Debt Service Coverage Ratio (DSCR) loans for long-term rental properties.


It rebranded to Kiavi in 2021, and in June 2025 became the first private non-bank institution in the US to reach 100,000 cumulative loans originated.


According to the official announcement, Kiavi achieved revenue of over $250 million and EBITDA of over $100 million in 2025, both record highs; full-year origination volume was approximately $7.8 billion, up about 20% from approximately $6.5 billion in 2024.


First Lien


The origin of Figure dates back to late 2017. After leaving SoFi that year, co-founder Mike Cagney turned his attention to blockchain. In 2018, he and his wife June Ou founded Figure in San Francisco. The following year, they launched their first product on their self-built Provenance Blockchain: a home equity line of credit (HELOC) originated on-chain.


Mike Cagney | Image source: Bloomberg


Subsequently, the company expanded along the same logic—loan origination, financing, secondary market trading...—and gradually migrated these processes on-chain, building the consumer lending marketplace Figure Connect and the on-chain warehouse financing market Democratized Prime.


Following its Nasdaq listing in September 2025, Figure officially stated that it currently accounts for about 75% of the global tokenized RWA volume.


The problem is, HELOCs are second-lien loans.


Second liens rank behind first liens in repayment priority upon borrower default, carrying higher risk and supporting a smaller asset scale. Figure estimates that the market size for first liens is about 25 times that of second liens.


Kiavi's RTL and DSCR products are both first-lien loans, and they operate in the Non-Qualified Mortgage (Non-QM) space, which traditional banks have long avoided due to regulatory concerns—a space with strong demand but highly fragmented supply.


Figure is proactively shifting the focus of its asset classes.


Post-acquisition, Figure expects to add over $7 billion in new first-lien loan volume annually. According to CEO Michael Tannenbaum, Figure's first-lien business proportion had already increased from 10% to 20% in 2025, and the company expects this ratio to reach about 40% by the end of 2027.


AI


Both Figure and Kiavi excel at using AI to process non-standard data that traditional financial institutions are reluctant to touch, building moats in areas where manual processes cannot scale.


Kiavi's core technological asset is a proprietary "after-repair value estimation engine" and an automated document review system. An old, dilapidated property awaiting renovation is nearly impossible for traditional institutions to risk-quantify.


Kiavi's model can predict the post-renovation market value based on historical transaction data and renovation plans, enabling the scaling of credit decisions for loans like RTLs.


This capability has yielded significant market advantages. According to the Scotsman Guide's "2025 Top Private Lenders" rankings (underlying data from Forecasa), Kiavi's fix-and-flip loan origination volume in 2024 was approximately $5.5 billion, more than three times that of the second-place lender, and it continued to expand its lead in 2025.


Image source: Kiavi


Figure aims to solve the problem of what happens to assets after they leave Kiavi: how to put them on-chain, how to circulate them, and how to attract institutional capital. Figure's newly unveiled product in this deal, Adaptor, is designed precisely for this purpose.


It supports "Agent to Agent" automated integration, standardizing the diverse data formats from different originating institutions, thereby compressing the onboarding cycle for new partners.


Kiavi's assets will become the first real-world validation scenario for Adaptor after its launch. According to Figure's investor presentation materials, the company expects this transaction to achieve approximately $35 million in cost synergies within 24 months.


Two AI systems are being stitched together, pointing towards the same goal: making non-standard real estate loans priceable, tradable, and scalable on-chain.


Integration


Figure employs a dual-class share structure. According to the IPO prospectus, Cagney and his affiliates hold Class B shares, controlling about 69% of the total voting power at the time of the IPO; as of the latest proxy statement in April 2026, he still controls the majority of voting power, and Figure continues to be designated a "controlled company" by Nasdaq.


As a growth-stage company undertaking a major acquisition less than a year after its IPO, Figure also disclosed in its S-1 filing that previously existing material weaknesses in internal controls were yet to be remediated. The large-scale integration poses a non-negligible test for the execution team.


On Kiavi's side, its assets are sensitive to interest rate cycles. During the 2022 hiking cycle, lacking the backing of government-sponsored enterprises (GSEs) like Fannie Mae and Freddie Mac, the liquidity of Kiavi's assets in capital markets tightened, leading the company to make corresponding cost and personnel adjustments.


This vulnerability has already been validated during the high-interest-rate phase, and future interest rate changes will remain a significant external variable for RTL/DSCR origination volume.


Furthermore, Figure's existing assets are still heavily concentrated in HELOCs. Kiavi's asset types, data formats, and customer base are significantly different. It remains to be seen whether Adaptor can truly reduce integration costs and whether Kiavi's non-standard assets can be smoothly absorbed by institutional investors on Democratized Prime.


The good news is that Figure stated the deal will be accretive to earnings per share, with an unlevered cash payback period not exceeding 4 years, and reaffirmed its medium-term EBITDA margin target of around 60%; Kiavi's monthly loan flow exceeding $100 million will be directly fed into Democratized Prime.


Sixth Street's managed and committed capital scale of over $130 billion, along with its $3 billion forward purchase commitment, provides considerable capital buffer for the joint venture; Mohan's entry into the executive team means Kiavi's customer relationships and industry resources are retained within Figure's management structure.


The RWA narrative has been around for several years. This time, Figure is using $717 million to migrate an institution that has operated for 13 years and processes billions of dollars in real loans annually entirely onto the blockchain. This is one of the most structurally significant acquisitions in the RWA tokenization space to date.


The potential market space Figure points to is its estimated approximately $200 billion annual RTL/DSCR origination opportunity, underpinned by the long-term renovation and rental demand generated by the roughly $25 trillion stock of aging housing in the US.


If the integration proceeds smoothly, this could be a landmark node marking the transition of blockchain capital markets from "proof-of-concept" to "scale operations." Not just for Figure, but for the entire RWA credit asset market.

Related Questions

QWhat is the main reason for Figure Technologies Solutions' acquisition of Kiavi?

AThe primary reason for Figure Technologies Solutions' acquisition of Kiavi is to pivot its business focus towards first-lien loans, such as Kiavi's Residential Transition Loans (RTL) and Debt Service Coverage Ratio (DSCR) loans. These loans represent a significantly larger market compared to Figure's existing HELOC products, which are second-lien loans.

QWhat is the significance of the 'Adaptor' product introduced by Figure in this deal?

AThe 'Adaptor' product is significant as it is designed to standardize and automate the connection of data from various loan originators, like Kiavi, onto the blockchain. It aims to reduce the integration period for new partners and facilitate the tokenization and trading of non-standard real estate loan assets on Figure's platform.

QWhat core AI-driven capabilities does Kiavi bring to the combined entity?

AKiavi brings its proprietary 'renovation-after-value estimation engine' and automated document review system. These AI capabilities allow Kiavi to assess the risk and predict the post-renovation market value of older properties, enabling the scalable underwriting of non-standard loans like fix-and-flip (RTL) loans.

QWhat are the major challenges or risks associated with integrating Kiavi into Figure's operations?

AMajor challenges include: 1) The integration complexity for Figure, a growth-stage company with recent IPO and past internal control weaknesses. 2) The interest-rate sensitivity of Kiavi's loan assets, which have shown vulnerability in high-rate environments. 3) The uncertainty of whether Kiavi's non-standard assets can be seamlessly absorbed by institutional investors on Figure's Democratized Prime platform.

QWhat makes this acquisition a structurally significant event for the broader RWA tokenization market?

AThis acquisition is structurally significant because it involves migrating a large, established, and profitable real-world lending platform (Kiavi) with over a decade of operation and billions in annual loan volume onto a blockchain ecosystem. It represents a move from conceptual proof-of-concept to large-scale operational execution in the RWA tokenization space, targeting a massive underlying market of U.S. housing renovation and rental demand.

Related Reads

Sequoia Dialogue with Jensen Huang: Computing Model Undergoes a 60-Year Transformation; You Won't Be Replaced by AI, But You Will Be Dimensionality-Reduced by 'Those Who Master AI'

NVIDIA founder and CEO Jensen Huang, in a conversation with Sequoia Capital's Konstantine Buhler, argues that we are witnessing the most significant computing shift in 60 years—from retrieval-based to generative computing. Instead of just storing and retrieving data, future systems will generate highly personalized content (text, images, video) on demand, powered by massive "AI factories." Huang envisions a global "intelligence network" that will envelop the planet, following the historical patterns of energy and communication grids. He outlines a five-layer investment framework: 1) Energy, 2) Chips/Computers, 3) Infrastructure (data centers), 4) AI Models, and 5) Applications. He predicts this ecosystem will reach a scale of $20 trillion annually. Crucially, Huang pushes back against fears of AI-driven job loss. He distinguishes between specific "tasks" (e.g., typing, analyzing images) and overall "jobs" (e.g., CEO, radiologist). While AI automates tasks, it increases efficiency and demand for the higher-value problem-solving aspects of professions, thus creating more jobs and "up-leveling" careers. The real risk, he asserts, is not being replaced by AI, but being outperformed by someone who effectively leverages it. He urges everyone to embrace AI as a tool for augmented capability and innovation.

marsbit20m ago

Sequoia Dialogue with Jensen Huang: Computing Model Undergoes a 60-Year Transformation; You Won't Be Replaced by AI, But You Will Be Dimensionality-Reduced by 'Those Who Master AI'

marsbit20m ago

"I Don't Need a Better Model Anymore": A Panorama of AI Users Under a Reddit Hot Post

Titled "I Don't Need a Better Model Anymore": AI User Reactions on Reddit Anthropic recently released Claude Fable 5, its first publicly available 'Mythos'-tier model, achieving 80.3% on the SWE-Bench Pro benchmark and significantly outperforming its predecessor and competitors. However, a viral Reddit post titled "Claude Fable made me realize I don't need better models anymore" highlighted a growing user sentiment of "good enough." Top comments expressed "model fatigue," with users stating that earlier models like Opus 4.5/4.8 already sufficed for their workflows. High cost was a key concern, as Fable 5's API is nearly twice the price of Opus 4.8, with users questioning the return on investment and suggesting the field has hit a plateau. The most frequent complaint targeted Fable 5's stringent safety filters. Designed to intercept high-risk requests (e.g., cybersecurity), the system was perceived as overly conservative. Users reported frequent rejections for routine security-related tasks, leading to automatic fallbacks to the older Opus model. Paying users were particularly frustrated, feeling they paid a premium for a less usable product. Dissenting voices came from users with heavy, complex tasks. For workloads like high-energy physics simulations with thousands of code lines, Fable 5's improved long-context understanding and error detection represented a significant, worthwhile leap—described as moving from a "college player to an NBA starter." The debate underscores a divergence between benchmark performance and practical utility. For most users, current models meet their needs, making further advances relevant only for extreme use-cases. The discussion also raised concerns about a potential "Public AI Freeze," where the most powerful models (like the restricted Mythos 5) remain exclusive to enterprises and governments, while public offerings stagnate. The launch presents two report cards: one of technical excellence and another of user skepticism. Fable 5's ultimate reception may depend on Anthropic's ability to refine its safety filters and justify its cost for specialized, high-demand users.

marsbit28m ago

"I Don't Need a Better Model Anymore": A Panorama of AI Users Under a Reddit Hot Post

marsbit28m ago

When AI Traffic Surpasses Humans, How Do You Prove You're Human?

With AI-generated web traffic surpassing human activity, websites face a crisis as AI agents bypass ads, avoid clicks, and scrape data without generating revenue. This disrupts the ad-based internet economy, diverting traffic and reducing site visits. In response, sites are blocking AI crawlers and deploying traps like Cloudflare's "honeypot" pages. Traditional CAPTCHAs are now ineffective against advanced AI. The focus has shifted to behavioral biometrics—analyzing unique human patterns such as cursor movement, typing rhythm, and keystroke dynamics. Companies like IBM and BioCatch use this data to distinguish humans from bots, even detecting fraud through behavioral inconsistencies. Two competing approaches aim to verify human identity centrally. Sam Altman’s World (formerly Worldcoin) uses iris scanning to create unique credentials, though it faces privacy concerns and regulatory bans. Alternatively, cryptographic zero-knowledge proofs offer anonymous verification without revealing personal data, championed by Vitalik Buterin to avoid centralized surveillance. However, both systems have flaws. Centralized solutions risk biometric data misuse, while decentralized models may be exploited through identity rental markets in economically unequal regions. Despite challenges, the author favors cryptographic methods for preserving privacy over pervasive behavioral monitoring that permanently captures and controls personal biometric data.

marsbit36m ago

When AI Traffic Surpasses Humans, How Do You Prove You're Human?

marsbit36m ago

2026 Landscape of Decentralized AI: Why is Blockchain the Inevitable "Antidote" for AI?

**The 2026 Landscape of Decentralized AI: Why Blockchain is the "Cure" AI Cannot Ignore** Decentralized AI addresses fundamental bottlenecks of centralized AI: scarce and expensive computational resources, excessive control concentration, unverifiable model outputs, and increasing difficulty in acquiring training data due to privacy and regulation. Blockchain offers a path to make intelligence open, verifiable, and economically accessible. The technical stack comprises three layers: 1. **Applications & Services**: The main crypto use cases are "Agentic Finance" (converting natural language into on-chain actions) and "Agentic Payments" for machine-to-machine commerce. Projects like Giza, Infinity Labs, Coinvest AI, and x402 (handling 173M+ transactions) are key players. 2. **Middleware**: This coordination layer enables agents to discover, identify, and transact. Notable projects include Gokite AI (specialized L1), Virtuals (an OS for the agent economy), and especially Bittensor—a network of specialized subnets forming competitive AI micro-economies. 3. **Infrastructure**: The capital-intensive layer providing raw resources. It includes decentralized compute (Akash, Render, Aethir), verifiable inference (Venice AI, OpenGradient), distributed training (Prime Intellect, Templar AI), decentralized storage (Filecoin, Walrus), and privacy/verification layers (Nillion, Arcium, Phala Network) using technologies like ZKPs, MPC, and TEEs. The outlook for 2026-2027 indicates AI demand outpacing infrastructure, with AI agents as a primary growth engine. Computation is becoming an asset class, with on-chain markets as its financial layer. Tokenomics is emerging as a structural advantage for coordinating capital, compute, and data in decentralized AI networks. While still early—with adoption uneven and revenue often trailing token incentives—projects like Bittensor, NEAR, and Virtuals demonstrate a shift from speculative narrative to a new model for coordinating intelligence.

marsbit39m ago

2026 Landscape of Decentralized AI: Why is Blockchain the Inevitable "Antidote" for AI?

marsbit39m ago

a16z Crypto Partner: Cash Flow is the Moat

Cash Flow as the Moat: A Playbook for Crypto Founders Historically, the most enduring businesses have been built by positioning themselves within the "flow of funds"—facilitating the creation and transfer of value in a network and extracting a portion of it. Cryptocurrency is the first modern technology natively built for this purpose. For startups, failing to architect products and businesses to leverage these principles means missing a major opportunity. Blockchains are inherently network businesses. Each transaction settles on a shared ledger, and every new participant strengthens the underlying network for all. Well-designed network tokens amplify this by aligning users, developers, and validators around growing the network, with value flowing back to contributors in a transparent feedback loop. This model is not new; companies from railroads and Standard Oil to Google, Meta, and AWS have thrived by inserting themselves into critical flows of value (goods, attention, compute). Financial markets make it even clearer: firms like Visa and major market makers generate immense revenue not by predicting markets but by being in the path of transactions. The combination of fund flow and network effects creates one of the most durable business structures. The high margins in traditional finance (payments, custody, lending, FX) represent prime targets. Crypto founders have the opportunity to build the next version—programmable, instant, global, and natively in the flow of funds. The frontier extends beyond finance to areas like computing/GPUs, AI training data, energy, robotics, and space—markets without entrenched intermediaries, ripe for building new, efficient value rails on programmable infrastructure. Founders should ask: Are you in the flow of funds today? Does your revenue scale 10x with the value of activity on your platform? Where in your target market are profit margins highest relative to value created? The opportunity is clear: embed your startup into the new flows of value and let the network effects accumulate.

marsbit41m ago

a16z Crypto Partner: Cash Flow is the Moat

marsbit41m ago

Trading

Spot
Futures

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of S (S) are presented below.

活动图片