One night. The data behind its price.

01 / Pricing intelligence

The example: a Friday in late January.

The retained example was checked on 10 November 2026 for a night on 29 January 2027, eighty days ahead. It draws on 79 bookings at the example home and 1,394 bookings at 15 selected similar homes. The wider eligible dataset contained 20,426 confirmed bookings across 248 historical homes.

Those are booking records, including confirmed future stays, not a count of completed stays. They are also not equally weighted votes. The model assesses relevance and the amount of usable evidence before combining them.

The resulting modelled booking-price estimate is about $925 for this night, before channel markup. That is one piece of pricing evidence. It is not a guest-facing quote, an automatically applied rate or a promise that the night will book.

The stored example also shows an estimated price range of about $695 to $1,230, before channel markup. It gives the team a way to see uncertainty around the estimate. It is not a guaranteed minimum or maximum booking price.

Bookings at this home

79

The home’s own history helps establish how it performs.

Bookings at similar homes

1,394

Evidence from 15 selected homes, weighted for relevance.

Modelled booking-price estimate

$925

Rounded. Before channel markup. Checked 10 November 2026; one anonymised example.

02 / Pricing intelligence

What makes those homes comparable?

We do not stop at the number of bedrooms and a nearby postcode. Matching considers property type, location, bedrooms, guest capacity, style and features such as pools, pool heating, spas, saunas, courts and beach settings. The model also learns from differences visible in actual booking evidence.

It selects relevant homes with enough qualifying booking history and gives more weight to closer matches. Where features differ, the model can make adjustments within set limits rather than pretending the properties are identical.

The point is to compare the experience a guest is choosing. A home with a distinctive outdoor area or a heated pool should not be treated as interchangeable with every home of the same size.

• The physical home

  Location, type, bedrooms, capacity, style and relevant amenities.

• The booking evidence

  What guests accepted, how much history exists and how closely the comparison fits.

03 / Pricing intelligence

How the home’s own history fits in.

A home with a substantial, useful booking history can tell us more about itself. A new home needs more help from relevant comparisons. PricingHub blends those sources rather than giving every property the same fixed balance.

In this example, about 68.5% of the blend comes from the home’s own evidence and 31.5% from similar homes. The 79 bookings at this home carry the same influence as about 43.6 full-weight bookings after the model applies its weighting. That describes how much influence the records have, not another set of bookings.

That blend establishes a starting price level of about $570 before the date-specific adjustments in this part of the model. It is not the rate we simply place on the calendar.

04 / Pricing intelligence

Then the date changes the picture.

A summer Friday and a quiet midweek night are different opportunities. The model considers broader seasonal patterns, regional differences and the shape shown by the home and its comparison group. Booking windows and stay length also matter to the assessment.

In this example, the late-January Friday/Saturday pattern moves the starting level to about $825. The regional seasonal adjustment takes it to about $887, and the remaining home/comparison seasonal pattern takes it to about $925.

Other factors do not all add a premium. The eighty-day booking window and two-night stay are reference categories in this calculation, so they add no further adjustment here. There is no additional event uplift for this night. Showing an unchanged factor matters as much as showing an increase.

Combined starting level

$570

Own-home and similar-home evidence, rounded.

Late-January Friday/Saturday pattern

$825

The broader date pattern is applied.

Regional seasonal adjustment

$887

The regional pattern refines the estimate.

Home/comparison seasonal pattern

$925

The remaining pattern refines it again. All figures before channel markup; rounding can affect displayed totals.

05 / Pricing intelligence

The calendar keeps moving.

The team is deciding what to ask now as well as what booking evidence suggests. Time until arrival, recent booking pace, current occupancy, gaps between stays and the property’s agreed rules can change what deserves attention.

A valuable date months away may have time to find a guest at a stronger asking rate. A short gap approaching arrival needs a different discussion. Recommendations can change as bookings arrive and the available dates change. Minimum stays and agreed limits remain part of that decision.

The modelled estimate above is therefore distinct from the wider engine’s asking-rate recommendation. Keeping those stages separate helps the team understand a suggestion, question it and use it with context.

06 / Pricing intelligence

Built by our founders. Used by our team.

Owning the technology lets us develop it around the homes we manage and the decisions our managers need to make. It also lets us inspect the reasons behind an estimate, rather than accepting a number without the supporting evidence.

Our portfolio managers bring their experience of the home, its presentation, guests and owner priorities. We meet as a team to go through each property’s performance, what is booking, what remains available and what needs attention.

For an owner, the benefit is a more considered and responsive approach. We can identify a date that may deserve a stronger rate, notice when booking patterns differ from expectations and give useful gaps attention. That supports judgement; it does not guarantee occupancy or returns.