Author: Raphaël Rocher

Humans have never really valued objects. We value the shared stories a group of strangers agrees to believe about them: that a piece of paper is money, that a signature transfers a claim nobody can touch, that a company’s name is worth more than its assets. A truck is worth roughly what any other truck is worth, anywhere in the world. A demand system, a customer’s habit of calling the same company back, exists only because enough people keep believing it will keep working, which is exactly why buyers keep paying real money for it.

A private equity firm does not write a check because it likes trucks. A truck loses value the moment it leaves the dealer’s lot, needs new tires every couple of years, and sits half-idle most weeks. If a moving company’s real worth lived in its fleet, buying one would look a lot like buying a rental car company: capital-heavy, thin-margin, unloved by anyone chasing a return. Yet moving, relocation, and storage businesses keep changing hands for real money, sold by families who spent decades building them to buyers who have never lifted a couch.

That gap is worth sitting with, because it answers a question most operators never get asked directly: what is this business worth, and to whom? When an outside buyer with no sentimental stake in the brand puts a number on a company, that number appraises the business more honestly than anything the founder could write about it. Four real, documented deals in and around this exact industry point at the same answer, repeatedly, across two decades and several different kinds of buyer. None of them paid for the trucks. They paid for the routes, the contracts, and the demand system sitting behind the fleet, the machinery that keeps the phone ringing whether or not this particular owner ever answers it personally.

What Roark bought when it bought the fastest-growing moving franchise in the country

Roark Capital, an Atlanta-based private equity firm, acquired ServiceMaster Brands in 2020, a deal that industry deal-trackers value at roughly $1.55 billion. On August 3, 2021, ServiceMaster Brands used that platform to acquire TWO MEN AND A TRUCK, a family-owned moving franchise launched in Lansing, Michigan in 1985. The deal announcement described it as the fastest-growing franchised moving company in the country: more than 380 locations across 46 states plus Canada, the UK, and Ireland.

That franchise’s own structure argues against assuming Roark bought a fleet. The brand’s own franchise disclosure filings require a new Metro-market location to start with at least two trucks, purchased and financed by the local franchisee, not the franchisor. Every one of those trucks sits on an individual owner-operator’s balance sheet. Franchisees pay ServiceMaster the other direction: a royalty of 6% of gross revenue, a 1% contribution to a national marketing fund, and a technology and support fee on top. ServiceMaster’s CEO at the time, Elane Stock, described the fit as TWO MEN AND A TRUCK’s “deeply ingrained culture of customer service” and the “capabilities of their franchise network.” Neither phrase mentions a truck. The purchase price for the moving-company deal itself was never disclosed, but the mechanics of what changed hands are public: a brand, a training system, a national marketing engine, and a royalty stream collected automatically off hundreds of independent operators’ revenue, forever, without ServiceMaster ever owning a single vehicle.

The company sold four times that never owned a truck

Clayton, Dubilier & Rice formed SIRVA in 1998 to acquire North American Van Lines, added Allied Van Lines and Pickfords the following year, and took the combined company public on the NYSE in 2003. SIRVA filed for bankruptcy in 2008 and emerged as a private company owned by Aurora Resurgence and Equity Group Investments. Madison Dearborn Partners bought it in May 2018. In August 2024, a group of credit funds led by KKR Credit Advisors, Evolution Credit Partners, BlackRock Financial Management, and Indaba Capital Management took control in a recapitalization. Madison Dearborn and Relo Group stayed on as equity sponsors.

In every one of those four deals, the trucks belonged to somebody else. SIRVA’s own filings around its 2003 IPO describe a network of roughly 760 independent moving agents operating around 7,800 vehicles, not a company-owned fleet. A client book kept changing hands instead: by SIRVA’s own account at the time, the company served 38% of the Fortune 500 across 2,500 corporate accounts. It also carries a government-relocation arm still branded BGRS. BGRS holds a GSA Schedule 48 contract and states it has managed more than 147,500 federal relocations since 1984. SIRVA merged with BGRS in 2022. The combined company is now a strategic partner inside HomeSafe Alliance, the consortium led by KBR and Tier One Relocation that now runs household-goods moves for the U.S. Department of Defense. Four different kinds of capital bought the same asset, one after another: a leveraged buyout shop in 1998, a distressed-debt investor in 2008, a mainstream PE fund in 2018, and a syndicate of credit funds in 2024. Each one paid for a book of corporate and government relocation contracts that renews on roughly its own schedule, largely indifferent to which name sits on the ownership ledger that quarter.

A box, not a truck, makes the same point twice

Arcapita, a Bahrain-based investment firm, bought PODS in 2007 for $430 million. Arcapita went into Chapter 11 in 2012, and PODS was restructured under new management, reportedly including Atlanta’s Eagle Merchant Partners. Ontario Teachers’ Pension Plan then bought the company outright in February 2015 for more than $1 billion. That doubled Arcapita’s original stake in eight years.

PODS’ containers depreciate the same way a truck does: steel boxes sitting in a yard, repainted on a schedule, replaced when they wear out. A Canadian pension fund managing $140.8 billion in assets at the time of that deal was not chasing box depreciation curves. Lee Sienna, who ran the deal for Ontario Teachers, said PODS fit the fund’s criteria for “steady cash flow” and “long-term growth potential.” Pension-fund money paid up for the same combination twice in eight years: a scheduling system that tracks which of roughly 150,000 containers sits free on a given day, a brand that turned into shorthand for the whole product category the way Kleenex did for tissue, and a footprint spanning the US, Australia, Canada, and the UK. The actual containers just sat in yards, doing what steel does.

“Isn’t this just what private equity always does?”

Yes, mostly. Private equity has run the same asset-light, recurring-revenue playbook across plumbing, HVAC, veterinary care, and dental practices for most of the last decade. None of that is unique to moving. A skeptical operator reading this far is right to ask whether these four deals prove anything about the industry specifically, or just confirm what PE always wants everywhere.

This is not only a private-equity habit, either. Extra Space Storage, a publicly traded REIT, agreed in April 2023 to acquire its rival Life Storage in an all-stock deal worth $12.7 billion. The deal closed that July. The combined company carried more than 3,500 locations and an enterprise value near $47 billion, according to SEC filings and coverage at the time. A self-storage building is a simpler asset than a moving fleet: four walls, a roll-up door, no engine to maintain. Public shareholders still paid a premium priced on occupancy, location density, and customer retention, not on the replacement cost of the buildings themselves. Storage and moving sit right next to each other in how people experience relocating. Capital in both categories keeps landing on the same answer: pay for who already shows up, not for what they show up in.

The honest answer to the skeptic’s question still needs a real counter-example, not just more agreement. Private equity does buy physical trucking capacity directly, and recently. DC Velocity, a logistics trade publication, reported in March 2022 that PE-backed buyers were acquiring full-truckload carriers, specialized heavy-haul and reefer fleets, and freight brokerages at pace during the tight-capacity freight market of the pandemic recovery. Uber Freight paid $2.25 billion for Transplace in 2021, a deal built almost entirely around freight capacity and brokerage relationships in a market where capacity itself was scarce enough to price at a premium.

That deal is the exception that clarifies the rule rather than breaking it. In a spot-market freight crunch, hauling capacity is genuinely the scarce, sellable thing, so buyers pay for capacity. Household relocation does not run that way in most weeks of most years. Trucks and crews sit available across nearly every metro market outside the June-to-August peak. Nobody is short on capacity to move a three-bedroom house on a random Tuesday in March. The scarce thing is a reliable mechanism for filling that non-scarce capacity with a customer who already trusts the name enough to say yes on the estimate call. That mechanism is what every one of the four deals above paid for. Same investor logic in both markets. Different asset, because a different thing is actually scarce.

The same $500,000 operator, priced two ways

This publication has modeled an illustrative operator before, and it holds up here too: a hundred moves a year at $5,000 average revenue, $500,000 a year, run out of a modest yard. A buyer pricing that business on fleet alone might count eight aging 26-foot box trucks, worth something like $30,000 to $50,000 apiece on the resale market once depreciation and mileage are accounted for. The fleet’s resale value comes to roughly $320,000 in total, about two-thirds of one year’s revenue, and it’s gone the moment the trucks are sold off or handed to a new owner who values them the same way. That is a rough illustration, not a quoted valuation, but it is the number a buyer arrives at when the fleet is genuinely all that is on offer.

The same $500,000 operator looks different with a Direct Demand Ratio of 45%, the metric this publication has argued operators should be tracking. Roughly $225,000 of that revenue arrives every year without a fresh acquisition cost: repeat customers, referrals, and organic search the business owns outright. Three corporate accounts under standing service agreements that renew automatically each January add to that total, the same kind of book SIRVA has been sold on four times over. That operator is not selling trucks at resale value. That operator is selling a demand system a buyer can plug new capital into and grow, the exact mechanism that made Roark pay for TWO MEN AND A TRUCK’s franchise network and made four separate investor groups pay for SIRVA’s contract book across twenty-six years. The trucks come along in both scenarios. Only one of them changes what a buyer is willing to pay for.

What a buyer’s diligence team actually asks to see

A real acquirer’s due diligence checklist for a business like this looks nothing like a fleet maintenance log. It asks for contract renewal history by account, going back several years, to see whether relationships hold or churn. It also wants to know what share of revenue comes from the top three referral sources or platforms, because concentration in one outside channel is a risk the buyer inherits on day one, and a federal lender already puts a number on where that starts. It asks for a Direct Demand Ratio, or something close to it under a different name, because a buyer wants to know how much of next year’s revenue is already spoken for before a single new dollar is spent finding it. It wants to see the CRM too, not to check whether one exists, but to see whether repeat-customer data has been tracked and used with any discipline. And it checks whether the business holds any standing government or corporate contract vehicles, and when those come up for renewal.

None of that shows up on a standard profit-and-loss statement, which is exactly why most operators have never been asked these questions before a buyer showed up asking them. A P&L records what the business earned. It says nothing about which part of that revenue would survive a change of ownership and which part would need to be rebuilt from zero. Those two numbers can differ by hundreds of thousands of dollars on a business the same size as the one above.

The SBA’s new rule tests whether the revenue survives the sale

Most operators reading this are not in a sale process, which makes a diligence checklist easy to file under someday. That filing is getting harder to justify. These questions are moving out of a buyer’s private judgment and into the rulebook that decides how much money a buyer is allowed to borrow.

The Small Business Administration’s revised lending policy, SOP 50 10 8.1, takes effect on October 1, 2026. For an initial acquisition or a business expansion where the purchase price reaches $3 million or more, it requires the lender to obtain an independent Quality of Earnings report before approving the loan. The SOP then specifies what that report has to cover. It “must assess the quality and sustainability of the business’s revenue base, including customer concentration risk, contract continuity, and the likelihood that existing revenue and margins will be maintained post-sale.”

That last clause turns a private buyer’s worry into written federal lending policy. An independent professional now has to put in writing whether the revenue keeps arriving once the founder stops answering the phone. And the consequence is financial rather than advisory: where the report’s numbers do not support the valuation and the proposed debt structure, the SOP says the loan amount “must be reduced accordingly.”

Three million dollars sits well above the illustrative operator modeled earlier, and a $500,000 business is not commissioning a Quality of Earnings report next year. The mechanism still reaches down, because it sets the ceiling on what a financed buyer can offer. A buyer whose lender will not fund the price does not pay the price.

The same document already puts a number on concentration in a different context. On SBA working-capital lines, receivables from any single customer above 20% of the total are kept out of the eligible borrowing base without the agency’s prior written consent, unless the account clears one of five narrow exceptions. A fifth of the total from one source is where a federal lender stops calling it a relationship and starts calling it a risk.

What the price tag was already telling you

The pattern across TWO MEN AND A TRUCK, SIRVA, PODS, and storage’s own consolidation wave holds whether or not any specific operator plans to sell next year, or ever: the market is grading, in real dollars, which parts of a moving business are worth building, something an owner’s own instincts rarely do. Nobody paid a premium for a well-maintained fleet alone in any of these deals. Every real premium sat on the demand side of the business, a franchise royalty stream, a federal contract book, a container-scheduling network, never on the labor and equipment side.

An operator who spends the next year adding a truck is spending money on the part of this business a buyer would write down to resale value on day one. An operator who spends that same year building a Direct Demand Ratio, a repeat-customer base, or a standing contract with even one corporate account is spending money on the only part of this business that has survived every one of these ownership changes intact. Movaros exists to help an operator build that second thing directly, the demand system a buyer would pay for, instead of waiting for someone else’s acquisition offer to be the first one to say so.

Nobody browses movers the way they browse anything they want to buy. A move gets bought once every five to seven years, usually because something else forced it: a job, a lease ending, a death, a divorce, a baby who needs a second bedroom. Marketers have a name for a purchase like that: a grudge buy, something a person needs and resents needing at the same time, bought with roughly the enthusiasm of a root canal.

The industry has its own favorite statistic about this, repeated in press releases and real estate blogs for years: that moving house is more stressful than divorce, more stressful than having a child, sometimes the single most stressful thing a person will do. The claim is popular. Its source is worth checking. Every version traceable online comes from consumer polling commissioned by moving and relocation companies, not from peer-reviewed research on stress.

The actual academic literature tells a smaller, more useful story. Psychiatrists Thomas Holmes and Richard Rahe built the Social Readjustment Rating Scale in 1967 from thousands of patient records to weigh how much life change different events demand. On that scale, a change of residence carries 20 life change units. Divorce carries 73. Death of a spouse carries 100. Moving alone doesn’t even reach the scale’s midpoint.

But Holmes and Rahe built their scale to be summed, not read one line at a time. A move rarely arrives alone. It typically travels with a change in job (36 units), a change in financial state (38), sometimes a new family member or the divorce that caused the move in the first place. Stacking three or four of those in the same season pushes the combined total into the range the two researchers associated with a measurably elevated health risk over the following two years. The honest version of the stressful-move claim has nothing to do with moving being uniquely catastrophic on its own. Moving is rarely the only thing happening. A sales process that only sees the move has no visibility into everything else stacked on top of it.

That’s the state a customer is usually in when six quotes land in an inbox: not necessarily in crisis, but in the middle of a genuine pile-up of simultaneous change, asked to make a judgment call about strangers. The line items all look about the same.

A good you can’t judge until it’s over

Economists have a specific term for a purchase like that. Michael Darby and Edi Karni described three categories of goods in a 1973 paper, sorted by when a buyer can judge quality. A search good, produce at a grocery store, lets a buyer assess quality before paying, just by looking. An experience good, a restaurant meal, requires consuming it once to know. A credence good is the hard case: the buyer often can’t judge quality even after paying and receiving the service, because they lack the expertise to tell a bad outcome from bad luck. Darby and Karni built the term around car repair and medicine, the two clearest examples of a seller who knows more about what’s wrong than the buyer ever will.

A move is a credence good with a truck attached. A customer comparing six quotes for the same three-bedroom interstate job is looking at numbers that appear identical on a screen. Almost nothing in those numbers shows which crew will wrap the good furniture properly, which will hit every delivery window, and which will find a reason to add three hundred dollars once the truck is already loaded. A customer can’t sample it in advance the way they’d taste a jam before buying the jar. Whatever they bought, they find out after it’s too late to choose differently.

A price-only marketplace treats a credence good as if it were a search good: line up the numbers, pick the lowest one, done. That isn’t a rounding error in an otherwise sound model. It’s a category mistake. The model prices the one thing a customer can compare, a number on a screen, while staying blind to the thing they can’t: whether that number represents the outcome they’re afraid of not getting.

What stress does to attention

A second layer sits under the economics, and it’s psychological rather than structural. Even when a customer understands, in the abstract, that price isn’t the whole story, they’ll still default to it under enough pressure. Pressure does that to attention generally, not just to movers.

Emotional arousal narrows attention. The psychologist J. A. Easterbrook described the mechanism in 1959. As stress rises, the range of cues a person can usefully process shrinks toward whatever feels most central and most immediately actionable, while everything peripheral, however relevant, drops out of view. Sendhil Mullainathan, Eldar Shafir and their co-authors found the same narrowing under any kind of shortage, not just money, in a 2013 study on scarcity published in the journal Science. People short on time or bandwidth showed measurably reduced cognitive function on tasks entirely unrelated to the thing they were short on. The researchers called it a bandwidth tax, because simply managing the shortage consumes the capacity a person would otherwise use to think clearly about anything else.

Here’s the actual week a move usually happens in. Three weeks’ notice on a relocation. A lease to break, a new one to sign sight unseen, a school to coordinate, a job that doesn’t pause for any of it. Six quotes arrive somewhere in the middle of that week, each one a wall of line items: packing materials, valuation coverage, fuel surcharges, and the binding-or-not fine print. Comparing all of that carefully would take real, focused attention. So little is left by the time the quotes land that the eye goes straight to the one number every quote shares and that takes no expertise at all to compare: the total at the bottom.

A related finding explains why the customer doesn’t just narrow toward price, but also rushes to end the search altogether. Starting in the 1990s, the psychologist Arie Kruglanski documented what he called the need for cognitive closure: a preference, sharpened by uncertainty and discomfort, for any firm answer over the continued discomfort of an open question. Under high closure need, people search less thoroughly and seize the first tolerable answer rather than keep looking, even when more looking would serve them. Someone drowning in an open decision doesn’t only narrow toward the cheapest number. They also want the whole decision to be over. Picking a number is the fastest way to make an open question feel closed.

More quotes, worse decisions

Price-anchoring under stress explains why a customer defaults to the cheapest number. A separate, well-documented finding explains why handing them more of those numbers doesn’t help.

One of the most cited experiments in consumer psychology came from the psychologists Sheena Iyengar and Mark Lepper in 2000. At an upscale grocery store, they set up a tasting table on alternating days: a limited display of six jam flavors on one, an extensive display of twenty-four on the other. The larger display drew more browsers: sixty percent of passing shoppers stopped, against forty percent at the smaller table. It converted far fewer of them into buyers. Only three percent of tasters at the twenty-four-jam table bought a jar. Thirty percent of tasters at the six-jam table did. More options didn’t produce more confident buyers. It produced more people who walked away having decided nothing.

The marketing scholar Henry Assael mapped how consumers behave across different kinds of purchases and named the exact profile a move fits: high involvement, meaning the stakes and the cost of a wrong call both feel real, combined with low perceived differentiation, meaning the buyer genuinely can’t tell the options apart. Assael’s label for that combination is dissonance-reducing buying behavior. It comes with a predictable symptom: heavy post-purchase doubt and a real appetite for reassurance afterward, because the buyer never got the confidence beforehand that the choice was even distinguishable from the alternatives.

Without meaning to, a marketplace that proudly advertises six comparable quotes has built close to the exact condition Iyengar and Lepper found performs worst. Assael’s finding makes it worse, not better. Those six quotes aren’t meaningfully differentiated to the person reading them, because, as established above, they can’t judge the one thing that differs: the execution. Six indistinguishable options combine the choice-overload condition and the dissonance-reducing condition on exactly the purchase where getting it wrong is expensive and hard to undo.

What compression costs

The instinct when a sale runs slow and uncertain is to speed it up: answer faster, quote faster, push for a decision before the customer can shop around. Some of that is good practice. Past a point, though, compressing a genuinely complex decision doesn’t make a customer more decisive. It makes them default.

The researchers Jonathan Levav, Mark Heitmann, Andreas Herrmann and Sheena Iyengar ran a field experiment in 2010 with real buyers ordering custom cars and suits at real German dealerships. Customers worked through dozens of sequential choices: four styles of gearshift knob, thirteen wheel rim options, dozens of engine and gearbox configurations, fifty-six interior colors. The researchers placed the most complex decisions, the ones with the most sub-options, early in the sequence, before fatigue had set in. Customers who saw that order engaged with them properly and customized more. When the researchers pushed those same complex decisions toward the end of a long sequence, after fatigue had built up over the easier early choices, customers increasingly just took the default and spent less. The order changed what people bought, not because the options changed, but because fatigue decides whether a customer is still choosing by the time the decision that matters most finally arrives.

A rushed moving quote reproduces the losing sequence on purpose. An estimator racing to close before the customer can shop the competition ends up pushing the decisions that matter toward the fatigued end of the call: coverage limits, what happens if a delivery window slips, whether the binding estimate truly binds. That’s exactly where a stressed, bandwidth-taxed customer is most likely to default rather than decide. The yes that comes out the other end looks like a closed sale. Often it’s a default nobody actually chose. Defaults don’t hold up the way decisions do, not once the customer’s attention returns to normal and they reread the fine print with a clearer head. That’s the mechanism behind something already true of this business: the quote dies anyway, later, quietly, without ever telling anyone why. It was rarely the price. It was a default wearing off.

Fast response isn’t the same as a fast close

An operator reading all of this has a fair objection: the data on speed is real too, and it points the other way. James Oldroyd, Kristina McElheran and David Elkington published a widely cited 2011 study in Harvard Business Review on how quickly companies responded to inbound sales inquiries. Reaching an enquirer inside the first hour made a firm nearly seven times likelier to qualify the lead than waiting a single hour more; let a full day pass, and the odds fell more than sixtyfold. Close to four in five sales went to whichever company responded first. That finding isn’t in dispute, and it doesn’t argue against anything in this piece.

What that research measures is response latency: how long a customer waits before anyone picks up the phone. What this piece argues against is decision compression: how much time a customer gets, once someone is on the phone with them, to work through a choice they can’t fully evaluate on price alone. Those are two different clocks. A sales process that confuses them tends to get the trade wrong in both directions: slow to show up, then rushing once it finally does. The fix isn’t a slower response. It’s answering immediately, then giving the later, harder conversation all the time it genuinely requires. That conversation involves a credence good, bought under a bandwidth tax, by someone whose attention has already narrowed to a single number.

Selling certainty, not cubic feet

What does that slower conversation contain? Here’s a woman moving her mother out of a house the family has owned for forty years, into a two-bedroom apartment near the mother’s new doctor. The move itself is routine by any operational measure: one truck, one crew, a few hours of driving. What the daughter is anxious about doesn’t show up on a cubic-footage estimate at all: whether the crew will be careful with a piano nobody plays but nobody can bring themselves to sell, whether they’ll understand why half the boxes are labeled in her late father’s handwriting and shouldn’t be repacked or relabeled, and whether the delivery window will hold, because her mother can’t manage two nights in a hotel at eighty-four.

None of that sits on a quote sheet. An estimator hitting call volume, reciting cubic feet and valuation tiers on autopilot, will miss every one of those signals and produce a technically accurate number that doesn’t answer the real question. An estimator who asks what she’s worried about, and answers it honestly, does more than provide customer service. That estimator sells the only thing she can verify in advance. She’s buying a credence good, Darby and Karni’s category, still holding here, so she can’t judge the quality of the execution before it happens, and often can’t fully judge it afterward either. The conversation is close to the only real signal she gets before she has to commit. Given what she can and can’t know in advance, reassurance is the most rational thing she has to go on, not a soft extra bolted onto the sale.

What your close rate can’t tell you

A sales dashboard can show how fast a quote closed. It has no column for whether the customer who said yes on Tuesday still believes it by Friday, once the adrenaline of the moving week has faded and their attention, no longer taxed by a dozen competing deadlines, comes back around to reread what they agreed to. That number, the one nobody’s built a report for, decides whether the sale holds.

Every mechanism here points the same direction. The purchase is a credence good, not a commodity. The buyer’s attention is narrowed and closure-hungry, not lazy or indecisive. Six comparable options reliably produce worse decisions than fewer, better-explained ones. A compressed decision produces a default, not a choice. Taken together, these mechanisms point toward two specific, separate disciplines: spending speed where the research says it pays off, on how fast someone answers the phone, and protecting time where the research says it’s needed, on how long the person on the other end gets to decide.

A sales process built to close fast is optimizing against the psychology of the person it’s trying to close. A sales process built to survive the Friday reread is optimizing for the only thing that was ever being sold.

For most of our species’ history, deciding whom to trust with something irreplaceable wasn’t a written contract question at all. It was a face a person recognized, a name a neighbor could vouch for, a reputation built over years in a village too small to fake one. Modern life scattered those small trusted circles across cities and time zones, leaving only the size of the circle changed, not the need for one. A moving estimate call is one of the last places a stranger is still asked to extend that older kind of trust to another stranger inside a single phone call. What replaces a village’s memory when the two people on the line have never met and never will again?

Every small business that trades reach for control is making a bet as old as the marketplace itself: that a bigger audience is worth whatever price gets extracted from the relationship later, once the party on the other side has enough leverage to name it. Hotels ran that bet to its conclusion in real time, in full view, over twenty years. In the early 2000s, independent hotels started listing on the platforms that would become Expedia and Booking.com. The leads were real, the guests were real, and the math looked fine: pay a commission, fill a room that would otherwise sit empty.

Two decades later, hospitality industry research routinely puts standard OTA commissions in the 15 to 25 percent range, sometimes higher for smaller properties with less negotiating leverage. Hotels have spent the better part of fifteen years running “book direct” campaigns, offering their own loyalty perks and lower rates. They’re trying to claw back a relationship they gave away one listing at a time.

Nobody signed up for that outcome. Each individual decision to list on a platform made sense in the year it was made.

The same curve, earlier

Moving and relocation marketplaces are running the identical early chapters right now. The escalation is documentable across three of them: Moving.com shows a customer up to four competing movers per enquiry, Sirelo shows up to five, Relocately shows up to six. That’s not a coincidence of platform design. It’s the same mechanism hotels lived through: more competing options on a page increase the platform’s leverage over everyone listed on it, and push price to the front of a decision that once included more than price.

Commission structures in this category haven’t been published the way hotel OTA commissions eventually were, so there’s no single number to cite here. What’s visible is the shape of the curve, and the shape is the point. Hotels didn’t lose their industry to OTAs by making one bad decision. They lost ground gradually, while the leads kept arriving and the math kept looking fine, right up until the aggregate cost of the relationship became visible only in hindsight.

What that curve looks like on the page itself is simple. A comparison grid ranks movers by price or response speed by default, with review scores as a secondary filter. Years in business or crew training don’t factor in at all, unless an operator pays to feature them. A customer scanning six quotes in a browser tab spends most of that attention on which number is lowest, not which company handled a piano move without a scratch three years ago. The layout isn’t hostile to any single operator. It’s optimized for the platform’s own conversion rate, which rewards whichever design gets a form submitted fastest.

Why the flat commission number understates what happened

The 15 to 25 percent figure hides the detail that explains the damage: the commission rate barely needed to move for the total cost to turn serious. What moved was the share of bookings running through the channel.

Picture a mid-sized independent hotel listing on an OTA at 18 percent commission, the middle of the trade-press range above. In its first year on the platform, the OTA might account for 10 percent of the hotel’s room-nights, mostly last-minute fill for rooms that would otherwise have gone empty. At that share, the commission costs the hotel roughly 1.8 percent of total revenue: a rounding error next to the extra occupancy it bought.

That 10 percent share rarely stays at 10 percent. Guests who book through the platform once tend to search it again for their next trip, because the reviews, the loyalty points, and the price-comparison habit all live on the platform’s side of the relationship now, not the hotel’s. Five years in, that same hotel might be sourcing 40 percent of its room-nights through the OTA at the same 18 percent rate. The rate never changed. The bill did: 40 percent of revenue at an 18 percent commission works out to 7.2 percent of total revenue, four times the original cost, without a single renegotiation.

This is a modeled illustration built on the commission range already cited above, not a published case study of a named property. The mechanism it illustrates holds regardless of the exact figures: a flat commission rate compounds with channel share, and channel share moves quietly while everyone watches the rate.

Moving marketplaces are built to produce the same compounding. A customer who requests four, five, or six quotes through a comparison page has just spent the one interaction that would normally build brand memory on the platform’s interface, not any single mover’s. The next time that household, or a friend of theirs, needs to move, they remember the platform and search there again. Share grows the way it grew for hotels: not because any single fee went up, but because the habit of starting the search on the platform got reinforced one transaction at a time.

The lock-in that hasn’t arrived yet

Hotels didn’t just lose share gradually. At various points, standard OTA contracts included rate-parity clauses: terms barring a hotel from advertising a lower price on its own site than the price listed on the platform. These weren’t rare or short-lived. Booking.com ran a version of the clause in Germany from 2013 until the country’s competition regulator ordered it dropped in 2015, and in December 2025 a Berlin regional court found Booking.com liable to compensate more than a thousand German hotels for the years the clause stayed in force, ruling that it had breached EU competition law. But for years the practical effect was direct: a hotel’s own website could not legally beat the commission-bearing listing on price. That meant a cheaper direct channel wasn’t just slow to build. It was contractually blocked.

Nothing published shows a moving marketplace using an equivalent clause today. But the incentive behind one is identical: a platform that owns the comparison moment benefits every time an operator’s own price can’t visibly beat the platform’s listing. A platform with enough leverage over enough operators eventually has the option to write that requirement into its terms, the way hotel OTAs did once independent hotels had nowhere else to source bookings. The absence of a rate-parity clause in this category today isn’t evidence the mechanism can’t happen here. It’s evidence the industry hasn’t yet reached the point where a platform has enough leverage to need one.

“A hotel guest comes back. A moving customer doesn’t.”

A real difference separates the two industries: most households move once every five to seven years, and plenty of movers never see the same customer twice in a career. Hotels can build a loyalty program because the same traveler books again next quarter. A mover generally can’t, because the next quote from that household is years away, if it ever arrives at all.

It means the exact playbook hotels eventually used (point systems, member rates, app-only perks) doesn’t transfer directly to moving. But the difference doesn’t break the parallel. It changes what the direct channel has to be built on.

Hotels rebuilt direct demand around the individual guest returning. Movers have to rebuild it around something else: the people around that one customer. A move is one of the more socially visible events in a household’s calendar. Neighbors notice the truck. Friends hear about the relocation before it happens. The customer who had a good experience gets asked “who did you use” by someone else within the year, not within a decade. Referral and reputation, not repeat purchase, are the moving industry’s version of the loyalty program. They build a channel a marketplace can’t easily insert itself into, because the recommendation happens between two people who already trust each other, off the platform entirely.

When operators treat their reviews, their referral flow, and their own site as an afterthought behind the marketplace lead queue, they’re skipping the one asset that played this exact structural role for hotels. It’s slower to build than a marketplace listing. It also can’t be bought back at 20 percent commission once it’s gone.

What actually happened, and what didn’t

Hotels didn’t disappear. The good ones are still full. What changed is who owns the customer relationship at the moment of booking, and how much of every dollar hotels now pay for someone else to hold that position. Direct-booking campaigns work, but they work slowly, against a platform with a decade’s head start and a much bigger marketing budget than any single property.

That’s the part worth sitting with before the moving industry finishes writing its own version of this decade. The fix isn’t refusing to appear on marketplaces; hotels that tried that mostly lost bookings without gaining anything back. The fix is for a business to build a demand channel of its own while continuing to use the marketplace, so the platform becomes one channel among several rather than the only door customers walk through.

What a demand channel is made of

“Build your own channel” is easy to say and vague enough to ignore. For a hotel, it eventually meant a fast, mobile-friendly booking engine on its own site, rate parity with the OTA listing so the direct price was never the worse deal, a loyalty program with a real discount attached, and a marketing budget aimed at past guests instead of new search traffic. Most independent properties spent years assembling that list, mostly because they started building it only after the dependency was already severe.

For a mover, the equivalent list looks different but isn’t shorter: a quote tool on the company’s own site fast enough to compete with a marketplace comparison page, a review capture process that puts finished jobs on the mover’s own domain instead of leaving them stranded on a lead-seller’s profile, a follow-up sequence for past customers timed to when their contacts are statistically likely to be moving, and a referral offer specific enough that a past customer remembers to mention it. None of this replaces marketplace leads on day one. All of it decides whether, five years from now, that operator is the 10-percent-share hotel from the model above or the 40-percent-share one.

Run that five-year model forward with two operators instead of one hotel, both starting from the same marketplace lead flow in year one. Operator A takes every marketplace lead that arrives and builds nothing alongside it. By year five, marketplace-sourced jobs might be 40 percent of the business. Each job pays whatever the platform’s lead fee has become by then. Operator A has no owned list of past customers to fall back on if that fee rises again. Operator B takes the identical marketplace leads in year one but reinvests part of the margin from them into an owned quote tool, a review page, and a standing referral offer starting that same year. By year five, if even a third of Operator B’s jobs come from repeat contacts, referrals, or organic search rather than the marketplace, that operator can walk away from a bad marketplace renegotiation that Operator A cannot. Same starting point, same leads in year one, a materially different position in year five, purely from where the second and third years of margin got reinvested.

Most movers already see this risk. What they haven’t had is the tooling to close the gap: an owned quote flow, review infrastructure, and follow-up automation. Building that historically required either a marketing budget or a development team, and a business running a fleet and a crew schedule doesn’t have the spare hours to manage either one. That’s a capability gap, not a willpower gap, which is why this argument ends at a platform decision rather than a to-do list.

Check the calendar, not the lead count

The clearest tell in the hotel story wasn’t the commission rate. It was the calendar. Properties that started building a direct channel in year two of their OTA relationship spent the next decade in a genuinely stronger position than the ones that waited until year twelve, by which point the platform already held most of their booking history, their reviews, and their customers’ habit of searching there first.

Moving marketplaces are still young enough that most operators are somewhere in year two through year five of their own version of that timeline: early enough that building a direct channel alongside the marketplace leads costs relatively little, and early enough that most competitors haven’t started either. That window doesn’t stay open on its own schedule. It closes at the same pace the hotel one did, one listing, one habit, one repeat search at a time, and it closes fastest for the operators who never checked what year they were in.