Proptech platform nHabit has launched a new agent-facing product designed to help landlords and letting agents price rental properties correctly first time. It uses proprietary data on which properties renters are rejecting when searching for a home. The launch is in response to the Renters Right Act, which removes a landlord’s ability to correct a pricing mistake once a tenancy has begun.
Since the first phase of the Act came into force on 1 May 2026, landlords and agents have been banned from accepting rental offers above the advertised price.
Alongside this, the abolition of Section 21 has removed the no-fault eviction route landlords previously used to correct a poor letting decision. Landlords and agents typically have a single opportunity per tenancy to set the right price and let to the right tenant.
nHabit’s new product has been built to address that challenge, giving agents visibility into rental demand and rejection data by postcode and property type. This is designed to give them a firmer basis for pricing decisions than they have today.
nHabit argues that every major property portal is built to capture what a renter likes, but none currently captures what a renter rejects. That gap is the basis for its new offering. The company believes this gives it a distinct position as other property portals move to bolt on third-party large language models to keep up with AI-led search.
The rejection data is drawn from nHabit’s consumer app, which launched in February 2026 and is set to reach 10,000 users in London by the end of September, with between 150 and 300 joining each day.
nHabit says the pattern of use also reflects a shift in what renters weigh up, with tenants placing more value on location, quality, transport links and long-term affordability alongside the monthly rent.
“We will understand renting like Tinder understands dating. The only way to get that level of understanding is through a swipe mechanism. With swipes you can see what people don’t like, whereas every other platform only captures what people like.”
Steven Charlton, Founder, nHabit
Steven argues that this ‘unsaid’ data, in the form of preferences renters never tell an agent but reveal through their behaviour, is what other platforms lack and are unlikely to build. This is because a swipe-based rejection model would represent too significant a departure for portals with an established consumer base and interface.
Franchisees from EweMove, the national franchise estate agency owned by The Property Franchise Group, are among the first agents on the nHabit platform. They will list rental properties on nHabit’s consumer app and use the demand and rejection reports to price instructions, having contributed to the reports during development.
“Get the price wrong now and there’s no second chance – too low and you’ll be buried in enquiries, too high and the property might sit empty for some time.
“nHabit’s demand and rejection reports will give us the local data to land on that sweet spot first time, when guesswork simply isn’t an option anymore.”
Nick Neill, Managing Director, EweMove Sales & Lettings
A key part of the product is a set of tenant personas, generated by Milo, nHabit’s proprietary large language model. The company comments these personas are not defined in advance but emerge from patterns in nHabit’s own data as volumes grow. ‘The personas are not something we define ourselves. They form based on the data as it gets more granular’ Steven said.
As the model is built on nHabit’s own data rather than an assumed demographic, the resulting personas vary by location, with the profile of a renter in London differing from one in Paris or Dubai. These personas are constantly updated as behaviour patterns emerge, rather than fixed at the outset.
The free tier of the agent dashboard has now launched, with the option to purchase pay as you go tokens to enhance the visibility of select listings for users. Agents onboarding during this initial phase will also have input into the data nHabit prioritises, giving early adopters a say in shaping the product.
