The Hidden Industry Paying $8K a Deal (Licensed in 2.5 Days)

Business summary

Mike Kortas presents NEXA as a mortgage-brokerage platform built around high loan-officer payouts, centralized operational support, and additional revenue streams beyond originating loans. His central strategy is to make the company more valuable to loan officers while using servicing data, AI, recruiting, and acquisitions to expand the broader business ecosystem.

Strategies and operating playbooks

• Build around the loan officer. Kortas says NEXA’s decisions should benefit loan officers, its primary customers. The company offers access to shared resources and amenities while allowing branches to retain their own names and marketing identities.
• Compete through differentiation, not imitation. Kortas says he avoids studying competitors’ tactics so NEXA can develop its own operating model. He emphasizes testing unconventional ideas with lawyers and regulators rather than relying on industry assumptions.
• Use a separate servicing operation to support retention. Kortas describes a separate company, Evolv Land, that works with mortgage servicing and allows loan officers to take on paid loan-retention work. He says that role can generate recurring service income and give loan officers data that may lead to future transactions. These are his descriptions of the structure; the legal and regulatory claims were not independently verified in the video.
• Turn servicing data into sales opportunities. The system can alert loan officers to signals such as a borrower’s credit being checked by another lender, a change in payment behavior, or possible plans to move or renovate. The stated goal is for the originating loan officer—not a call center—to follow up.
• Use AI to prioritize outreach. Kortas says AI can combine servicing and other customer signals, suggest what to say, and prompt a loan officer to respond quickly. If one officer does not act, the opportunity may be routed to another representative. He argues that AI should reduce administrative work and leave more time for relationship-building.
• Offer high payouts with a different revenue model. Kortas says NEXA can pay loan officers more than 100% of a loan’s stated revenue because the company earns through other activities, volume, and control of cash flows—not only the loan itself. He describes a branch ledger that can direct funds toward team growth, as well as retention payments to keep productive officers.
• Recruit with flexible economics. Rather than requiring long-term employment-style commitments, Kortas says NEXA can structure recruiting advances as loans that are forgiven as the recruit produces. His stated aim is to attract people who choose to stay, rather than tie them down with repayment-heavy contracts.
• Reduce the burden of running a brokerage. Existing brokerages can reportedly keep their names and branding while moving onto NEXA’s platform. NEXA takes on functions such as licensing, audits, HR, and compliance, which Kortas says reduces costs and administrative distractions for branch owners.
• Teach lead generation, not just provide leads. NEXA offers leads through some teams, but Kortas encourages loan officers to learn self-generation so they can keep more of the economics and build an independent book of business.
• Use non-licensed business development marketers (BDMs). The guest describes BDMs as people who promote the business and make referrals through personal networks, social media, or professional contacts. He gives examples including real estate agents, a roofer, a grandmother, hairdressers, and rideshare drivers. He says NEXA has had these arrangements reviewed by regulators; that claim was not independently verified.
• Acquire only businesses that fit the ecosystem. Kortas says he is not interested in acquisitions merely because they look financially attractive; they must also help loan officers or strengthen NEXA’s business ecosystem.

Metrics and figures cited

• $8,000 average earnings per loan was cited as a starting-point estimate. At one loan per month, the speakers said that could amount to more than $90,000 annually.
• Some loan officers reportedly close 10–20 loans per month, with larger teams closing more.
• A loan-licensing course was described as 20 hours, completable in about 2.5 days.
• Kortas gave an example of a 275-basis-point arrangement in which NEXA pays 305 basis points. He also described a branch-ledger example involving 200–300 basis points, with a loan officer receiving 2% and the remaining 1% allocated to a ledger for branch or team growth. These examples reflect his explanation of the model, not independently confirmed compensation terms.
• Kortas claimed his approach can earn NEXA an additional 57 basis points compared with what he says others in the industry know how to earn.
• He also claimed a payroll-related strategy returns 2.5% of each dollar paid through payroll, tax-free. This is a tax-related claim and should not be treated as verified tax advice.
• The AI system’s social-media-based prediction of likely home-buying or selling intent was described as about 80% accurate; Kortas said he did not recall the exact figure.
• A retention specialist may be paid monthly for work connected to keeping a loan in the portfolio for as long as 10 years.
• The guest said NEXA offers a weekly informational call every Thursday at 11:00 a.m. Arizona time.

Examples and case studies

• Servicing-to-refinance or home-equity opportunity: A borrower’s payment is briefly late, and a soft credit check indicates increased revolving debt. The loan officer can reach out about whether a HELOC or other product might better fit the borrower’s needs.
• Recruiting economics: Kortas contrasts large recruiting checks tied to long contracts with NEXA’s described approach of advancing money and forgiving it as the recruit produces.
• Brokerage transition: A brokerage can reportedly retain its public-facing brand while moving its back-office functions and compliance responsibilities to NEXA.
• FSBO.com acquisition: Kortas says he joined a deal to acquire FSBO.com after learning that the other party had contractual rights to buy it. He says the transaction closed in six calendar days.
• U Mortgage merger: The speakers cited the merger with U Mortgage as an example of a former competitor joining the platform.
• BDM referral marketing: A hairdresser, rideshare driver, or other networked person can introduce potential borrowers to a loan officer without originating the loan themselves, according to the guest’s description.

Actionable recommendations from the discussion

• Evaluate a brokerage based on total economics, support, and operational burden—not payout percentage alone.
• If moving into lending, use training and provided leads to get started, but learn self-generated marketing and prospecting over time.
• Treat relationship-building and prompt follow-up as core sales skills, with AI as a prioritization and productivity tool rather than a substitute for trust.
• For brokerage owners considering a platform move, compare licensing, compliance, HR, audit, and other overhead alongside compensation.
• Before adopting compensation, servicing, payroll, or referral structures, obtain independent legal, regulatory, and tax advice. Several of the guest’s explanations involve claims about compliance and tax treatment that should be verified for the specific business and jurisdiction.

Presenters and sources

• Presenters: Brad Lea, host; Mike Kortas, NEXA founder and guest.
• Other sources mentioned: NEXA University; the “Why NEXA” weekly call; NEXA’s servicing and AI systems; Cardiff, the episode sponsor.