We have been managing holiday rentals in Phuket since 2008, and in that time pricing a property has become much easier, quicker and more reliable.
Modern pricing tools can respond to demand, availability and how close a date is to arrival. Instead of setting a few broad seasonal rates and updating them by hand, we can now price each night individually and adjust booking rules across the calendar.
That does not mean we leave the decisions to software. The tools are much better, but they still need to be set up properly, checked regularly and balanced with what we know about Phuket and each property we manage.
Before dynamic pricing tools were available, the process looked very different.
Before We Used PriceLabs
Setting the rates for every property used to be a manual job. We would search Google and online travel agencies such as Airbnb, Booking.com, Expedia and Tripadvisor to find similar properties, then compare both their prices and their seasonal dates.
We would then set three main rates: a low-season rate from 1st May until the end of October, a high-season rate from 1st November until the end of April, and a peak-season rate from 15th December until 15th January.
Those rates still needed to be reviewed as bookings came in. If a property was booking faster or more slowly than expected, we would return to the competing listings, check the market again and update the rates.
This took an enormous amount of time, particularly as our portfolio grew. However, it was how many owners and property managers approached pricing before dynamic-pricing tools became available.
Why Fixed Seasonal Rates Had Their Limits
Phuket has recognisable seasons, but demand does not stay the same throughout each one.
March and April, for example, are normally described as high season but are typically much quieter than January and February. July and August are usually the busiest part of the low season and can perform very differently from May, June, September or October.
One price for an entire season cannot reflect those changes. It also cannot respond quickly if a property books faster than expected, has too many dates still available or develops short gaps between reservations.
Dynamic pricing gives us much more flexibility. It does not, however, mean accepting every price the software recommends.
How We Set Up a New Property
When a property first joins our rental programme, PriceLabs is not yet connected. We therefore set temporary seasonal rates using comparable properties that we already manage. These rates are normally only in place for a day or two, until the PriceLabs setup is ready.
Once connected, PriceLabs recommends an initial base price using market data. Location is part of that comparison, but it needs to be more specific than simply matching the property type and general area. Not every one-bedroom condominium in Kamala should be compared with every other one-bedroom condominium in Kamala. The development, exact location, view, size, furnishings, facilities and likely guest type can all affect its pricing.
At this stage, we do not usually need to repeat the comparison manually. PriceLabs shows us the market it has used, allowing us to check whether its pricing makes sense for the property.
Once the listing starts receiving bookings, its own performance contributes to the recommended base price alongside the wider market. The recommendation therefore becomes more specific to that property as booking data builds.
We then set a minimum and maximum nightly rate for the property. The minimum prevents automated discounts from taking the price below an amount we consider sensible. The maximum stops unusually high recommendations from causing problems during peak dates.
What PriceLabs Does for Us
PriceLabs helps us adjust prices across the calendar based on factors such as seasonality, market demand, occupancy and how close the date is to arrival.
We also use it to manage:
- Last-minute discounts
- Far-ahead pricing
- Early-booking adjustments
- Minimum-stay requirements
- Short gaps between existing reservations
- Different pricing adjustments for Airbnb and Booking.com
It saves us a considerable amount of manual work, but it is not something we set and forget. PriceLabs handles the calculations and applies the rules; we remain responsible for deciding whether the result makes sense for the property.
Our Weekly Pricing Review
Every week, we carry out a formal pricing review across the portfolio.
We look at new reservations, current occupancy, remaining availability and any gaps between bookings. We also check for nightly rates that appear unusually high or low and whether the settings are producing the results we expected.
The purpose is not to override every automatic adjustment. It is to find the dates and properties that need our attention and make changes when the software's recommendation does not match what we are seeing.
How Our Last-Minute Discounts Work
Once an available night has passed, it can never be sold. As the arrival date gets closer, accepting a lower rate can make more sense than holding out for a price that may leave the property empty.
Where they are active, our occupancy-based last-minute discounts begin 14 days before arrival. The discount increases as the date gets closer and also takes the property's occupancy into account.
We do not use the same last-minute discount settings throughout the year. Low season and high season have different settings, and during the busiest high-season months we do not apply a last-minute discount at all.
A property that is already well booked also does not need the same adjustment as one with plenty of availability. The aim is not to discount every empty night automatically. It is to lower the rate when doing so has a reasonable chance of generating additional bookings and owner income.
How Our Early-Bird Pricing Works
For dates six months or more in advance, we increase the PriceLabs rate by 10%.
We then manually set a 10% early-bird discount in the Airbnb and Booking.com dashboards. We use this arrangement for every property.
The platforms can display the discount in their searches and promotions, while the 10% increase keeps the amount paid by the guest close to the rate we intended to offer.
Why We Usually Set a Three-Night Minimum
Most of our properties normally have a three-night minimum stay. This gives us a sensible balance between keeping the property available to guests and avoiding too many very short bookings.
Every reservation involves preparing and inspecting the property, cleaning, laundering the bed linen and towels, communicating with the guest, and managing check-in and check-out. Much of that work is the same whether the guest stays for one night or one week.
However, we do not want the usual minimum stay to make a short gap between two longer reservations impossible to book.
If two confirmed reservations leave a two-night gap, a three-night minimum would block those dates. We use PriceLabs' orphan-gap settings to reduce the minimum stay to the length of the gap. In this example, the remaining dates would automatically become available for a two-night booking.
The same can apply to a one-night gap when accepting it makes sense operationally. The rule helps us increase occupancy without changing the property's normal minimum stay across the rest of the calendar.
Accounting for Airbnb and Booking.com Commissions
The rates we send to Airbnb and Booking.com are adjusted to account for each platform's commission.
At present, our Airbnb prices are increased by 15.5% and our Booking.com prices by 17.5%. These adjustments are included in our PriceLabs setup.
This means the cost of receiving a booking through each channel is considered when the advertised rate is calculated. If either platform changes its commission structure, we can update the adjustment as part of the pricing setup.
What We Learnt From New Year Bookings
New Year gave us one of the clearest examples of why dynamic pricing still needs human oversight.
When we first started using PriceLabs, guests would sometimes book Christmas and New Year dates in June or July, when the far-ahead recommended rates were particularly high. They could hold the reservation until approximately 30 days before arrival and then cancel within the platform's free-cancellation terms.
PriceLabs would recalculate the newly available dates using the much shorter booking window, which resulted in a lower rate. The guest could then book the same dates again for considerably less than the original reservation.
This was not a discount we had chosen to attract a replacement booking. It was the result of PriceLabs recalculating the rate after the cancellation.
We solved the problem by setting maximum nightly rates. Since adding those limits, we have not experienced it again.
The software had followed its rules correctly, but the result was not working in the owner's interests. It was a useful lesson in why automated pricing still needs limits and regular checks.
Why We Avoid Promising a Particular Income
A rental-income projection depends on assumptions about both price and occupancy. A high projected nightly rate means very little if guests are not willing to book it.
Results also depend on the property's condition, reviews, location, facilities, competing availability and how far ahead guests tend to book. We can assess these factors, but we cannot guarantee the outcome.
We would rather explain how we intend to position, price and manage a property than use an inflated projection to make the opportunity look more certain than it is.
PriceLabs allows us to react faster and price each property in much more detail than the old manual system. Local knowledge, sensible limits and regular review are still what turn the technology into a useful pricing strategy.
You can read how pricing fits into the wider rental operation in How We Market Your Phuket Holiday Rental.
If you own a villa or condominium in Phuket and would like us to assess its rental potential and management requirements, get in touch.
