Which channels produced signed lease-purchase agreements?

Rent-to-own attracts households who cannot get a mortgage today, which means a very high inquiry volume and a very low qualification rate. Dozens of inquiries per signed agreement is normal rather than a failure.

At that ratio, cost per lead is close to meaningless and can be actively harmful: the cheapest leads are usually the least likely to qualify, and optimizing toward them makes the business worse while every dashboard improves.

Ranking channels on signed agreements, option fees and the rent actually collected over the option period is the only comparison that reflects what the business earns.

An option fee is collected up front, a rent premium accrues monthly through the option period, and a purchase — or a forfeited option — closes it out. All three belong to the channel that produced the household.

Matching on the household's phone or email rather than on a transaction keeps the whole sequence attached to one acquisition, so a channel is judged on the full value of what it produced rather than on the first check.

The funnel has two constraints and most reporting only knows about one. Households have to qualify, which is slow and rejects the majority, and a suitable home has to be found, negotiated and purchased, which depends on inventory, price and whatever the seller decides on the day. An approved household waiting four months for a house that never materializes is a marketing success and a business failure at the same time. Both constraints are real, they move independently, and only one of them is visible in any advertising account.

When acquisition outruns inventory, more inquiries make things worse rather than better. Approved households go cold, the pipeline fills with people who were promised something the company cannot currently deliver, and the reviews that follow make the next month's advertising more expensive. Nothing in a cost per lead report can indicate that the correct action is to spend less. It is one of the rare situations where the correct marketing decision is to slow down, and no lead-based dashboard has ever produced that recommendation for anybody.

Ranking sources on signed agreements and collected revenue keeps the measurement attached to the constraint. If approvals are rising and agreements are not, the report shows it as revenue that did not appear rather than as leads that did. Exporting the approval date alongside the agreement date makes the waiting time itself visible, which is the figure that should be governing how hard the top of the funnel is pushed. Approvals are a leading indicator and agreements are the outcome, and the distance between them is where this business either works or quietly stops working.

Rent-to-own is searched for nationally and delivered locally. The company buys homes in a handful of metropolitan areas, sometimes in specific price bands and specific school districts, and the inquiries arrive from everywhere. Households in states the company has never operated in, at price points it does not serve, for homes that do not exist in its markets, all of them taking up a call handler's time and all of them counted as leads. None of that volume can become revenue and all of it improves the cost per lead, which is the problem in a single sentence.

The proportion is high enough that national cost per lead is close to meaningless as a management number. Two campaigns with identical lead costs can differ by a factor of several in how much of that volume is even addressable, and the difference does not show up until somebody counts signed agreements by market — which is rarely anybody's job. The campaigns look equivalent on every screen the media buyer has access to, and they are not equivalent in any respect that matters to the business.

Building the ranking on agreements and collected revenue already strips out the inquiries nobody could serve, and a market column mapped as Location then shows where that revenue came from and how much of it traces to a channel. Spend can then be pulled back to the markets with inventory and pushed where the buying team is actually active, and an expansion into a new metropolitan area can be judged on what it produced rather than on what it attracted. The column shows where revenue is, not where inquiries were, but geography is often the largest single lever in this segment and the one least often present in the reporting that governs the spend.

A meaningful share of lease-purchase households do not complete. Circumstances change, credit does not repair as quickly as hoped, the option lapses or the household leaves partway through the term. That is a known feature of the model rather than a scandal, and it means any measure built on projected outcomes is describing a future that a substantial minority of these agreements will not reach. Households enter these agreements precisely because their circumstances are unsettled, and some proportion of unsettled circumstances stay that way.

It would be easy to rank channels on expected value: option fee plus rent premium plus assumed purchase, multiplied by some completion rate. It would also be wrong in a way nobody could audit, because the completion rate differs by household, by market and by year, and the number would be doing most of the work rather than the evidence. An assumption of that size, applied across every channel, would end up being the main determinant of the ranking rather than anything the marketing actually did.

The report counts what was actually collected and nothing else. An option fee received is revenue, each month's rent received is revenue, and a completed purchase is revenue when it happens. A household that walked away in year two contributes what it paid and stops. Channels are therefore ranked on money the company has, which is the only version of the figure that survives being checked against the bank. It is also why a source that produces households who keep paying ranks above one that produces households who sign and lapse, without anybody having to argue about likelihoods.