Context and Challenge
Guesty is an end-to-end platform for property managers to manage short-term rentals on sites like Airbnb, Booking.com, and Vrbo. Property managers use it to handle listings, reservations, pricing, and guest communication.
One of the biggest challenges that property managers face is setting the right price for each property on every day. Pricing is affected by many factors:
- Seasonality
- Weekdays vs weekends
- Local events
- Competitor pricing
- Property popularity
- Minimum stay duration
- Last-minute availability
Doing this manually is nearly impossible. It takes time, increases the risk of errors, and makes it hard to react to changes quickly. Many Guesty users were losing money or spending too much time adjusting prices manually. That’s why Guesty decided to build a price engine and involved Forbytes to for the development process.
Client’s Objectives
Guesty needed a flexible pricing tool that would:
- Set the right price for each day. Define pricing based on demand, market trends, local events, and other factors.
- Make pricing competitive. Compare listings with the local market and adjust prices to make the offers attractive.
- Automate price changes. Allow for the full automation of pricing updates and ensure prices are always up to date.
- Support smart rules. Let users set base prices, limits, and adjust prices based on seasons, events, or last-minute availability.
- Work across channels. Sync pricing to Airbnb, Booking.com, and other OTAs in real time.
- Be transparent and easy to manage. Show why the price changed and let users customize their strategy.
How We Approached the Project
The price engine project started with a Proof of Concept (PoC). Guesty’s data science teams worked on the formulas and coefficients. Forbytes joined the project to turn this idea into a working product.
Our approach involved the following stages:
- Discovery and analysis. We began the project by working with Guesty’s product owners, stakeholders, and data science team to define the pricing logic. Guesty’s BI experts researched the market and created a formula based on pricing coefficients that formed the basis for the price engine.
- Planning and prioritization. Together with the product and sales teams, we outlined the project roadmap. At this stage, we also prioritized which features should be developed first, based on user value and business needs.
- Proof of Concept (PoC). We built a PoC to test the main idea. It included the core logic of the pricing engine — such as setting a base price, adding seasonal adjustments, and configuring price limits. This helped validate our approach before building the full product.
- User testing and feedback. Guesty gave the PoC version to a group of users. These users tested the tool and shared their feedback. Together with Guesty, we carefully analyzed their input to learn what worked and what needed improvement.
- Iterative development. We used an iterative approach to build the full version of the engine. Based on feedback and new business requirements, we added new features step by step. With each release, the engine became more powerful and easier to use.
- Continuous improvements. We added more customization options, refined pricing rules, and made the engine more responsive to changes in demand and market behavior. This helped Guesty keep its pricing competitive and smart.
- Support and maintenance. Our team continues to support Guesty by fixing bugs, adding features, and making sure the engine works smoothly with all other parts of the platform. We also monitor how the tool is used and make adjustments based on new feedback or changes in the market.
Thanks to this approach, we delivered a robust, scalable, and intelligent pricing engine that meets the real-world needs of property managers. It’s fast, flexible, and built to adapt as Guesty’s business grows.
Solution Overview
At the heart of the engine is a pricing algorithm created by Guesty’s data science team. Their formulas are based on deep market research, historical booking data, and continuous analysis of supply and demand.
Our team took the formulas and built a full solution — including the backend logic, user interface, and integration into Guesty’s platform — to allow property managers to control and customize their pricing strategies easily.
Here’s what the price engine offers:
1. Full Market Visibility
Users can see a complete picture of their pricing performance and forecasts. On a single screen, they can view:
- Their current pricing
- Suggested new pricing
- Market-based pricing
This makes it easy to track how prices change depending on the day of the week, holidays, local events, or the season (e.g., high season vs low season). This visibility helps users stay updated and make quick decisions.
Image source: guesty.com
2. Customizable Pricing Settings
One of the biggest strengths of the engine is that it’s highly configurable. Property managers can adjust the engine to match their needs. The algorithm gives suggestions, but users stay in control.
They can customize:
- Base price: Set rules that include holidays, seasons, weekdays, and last-minute strategies.
- Price limits: Define the highest and lowest rates they’re willing to charge — either for a whole year, a season, or for specific dates.
- Minimum nights of stay: Set the least number of nights a guest can book — useful for weekends, holidays, or event seasons.
- Last-minute pricing: Automatically lower prices when the stay date is approaching and the unit is still available.
- Upcoming availability: Adjust prices based on how full the calendar is.
- Event-based pricing: Add custom rules for specific events in the city to boost prices when demand is high.
Image source: guesty.com
These settings help avoid empty dates and ensure each property earns as much as possible, even during slow periods.
3. Embedded Dynamic Pricing
The pricing engine is deeply embedded into Guesty’s system. It works in the background to update prices across all listings. It also syncs with external platforms like Airbnb or Booking.com, keeping everything consistent and up to date.
The system adjusts prices automatically, but users can manually override it if needed. Managers can easily respond to reservation loads and market trends without starting from scratch.
Image source: guesty.com
4. All-in-One Dashboard
Managers can control every part of their pricing strategy from a single screen. This includes:
- Revenue goals
- Distribution channels
- Occupancy tracking
- Manual price overrides
Having everything in one place saves time and helps prevent errors.
Image source: guesty.com
5. Smart Promotions
The engine also suggests special offers — like discounts for longer stays or last-minute bookings. These promotions are personalized for each listing based on how likely it is to be booked. After they are created, the system sends the updated rates to connected channels automatically.
6. Competitor Rate Shopper
Managers can define their own competitor groups or markets, so they always know how their pricing compares and when to react to stay competitive in high-demand periods. They can:
- Track market occupancy
- Create competitor groups
- Set market benchmarks
The pricing engine then uses this data to improve the pricing strategy automatically.
7. Personalized Pricing Per Property
Every listing is unique. The pricing engine considers things like:
- Location
- Property size
- Guest reviews
- Amenities
So, even if two properties use the same tool, they get different pricing strategies. This ensures a fair and competitive price for each listing, helping managers stand out on busy platforms.
Detailed Execution Timeline
The project was split into key phases:
- Planning and discovery: worked closely with product, BI, and data science teams
- PoC creation: created base logic, rules, and first configurations
- User testing and feedback gathering
- Iterative feature rollout: set pricing rules, UI, OTA sync
- Enhancements and optimization
- Ongoing support and maintenance
Results and Impact
Qualitative results:
- Highly configurable pricing system. The engine offers flexible price-setting parameters. This level of configurability allows each user to define a pricing strategy that fits their unique business goals.
- Competitor Rate Shopper for better market positioning. We introduced a feature that monitors competitor listings and rates in real time. The price engine automatically compares this data to the user’s current rates and suggests adjustments.
- Real-time price updates. Our engine pushes price updates instantly. Whether it’s a sudden increase in demand, an upcoming event, or a last-minute cancellation, the system reacts in real time.
- BI-powered pricing decisions. The BI algorithm embedded into the system analyzes booking trends, market occupancy, and more. With these insights, managers can fine-tune their pricing rules and strategies to boost performance.
- Personalized pricing recommendations for every listing. Even if multiple properties use the same tool, each one gets a strategy tailored to its unique characteristics. This personalization gives users a competitive edge in crowded markets.
- 2-way integration with OTAs and Channel Managers. We built the price engine to sync effortlessly with major platforms and OTAs through channel managers. This means Guesty not only sends updated prices to the listings but also receives reservation and occupancy data from these platforms.
- Smart automation with human control. The pricing engine automates routine tasks like daily price adjustments and rule enforcement, which saves users hours of manual work.
Technology Stack
Backend: Node.js, Typescript, Nest
Frontend: React, Redux
Database: MongoDB, PostgreSQL
Test tools: Mocha, Jest, Chai, Sinon
Cloud platform: AWS, Google Cloud
Logs/Monitoring tools: Grafana, Prometheus, AWS CloudWatch, InfluxDB, Coralogix, Kibana, Datadog
Virtualization: Docker, Nomad
Continuous integration: Circleci, Jenkins, Split.io, GitHub, Vault, Jira
Other tools: Redis, Rabbit, MQKafka
Key Takeaways and Lessons Learned
Success factors:
- Strong partnership with sales and marketing teams. Our engineers worked hand in hand with Guesty’s sales and marketing departments. These teams have direct contact with users and understand what features are most needed and what pain points property managers face.
- Close collaboration with Guesty’s data science team. Guesty’s internal data science team provided the core pricing models, coefficients, and historical data analysis that formed the foundation of the pricing logic.
- Deep understanding of Guesty’s platform and business model. We spent time learning how the system works, how property managers use it, and how listings, reservations, and channels are connected.
- Feature development based on real user feedback. After launching the first version of the price engine, we collected feedback from early adopters. We prioritized features that users wanted most.
- Data-driven architecture and decision making. The architecture we developed ensures that data flows correctly between all components, from external channels to analytics dashboards.
Challenges overcome:
- Handling complex and inconsistent data. Guesty collects data from many sources like OTAs, calendars, and competitor listings. Forbytes built a strong data ingestion and cleaning pipeline to make sure all data is in the right format before it reaches the pricing engine.
- Balancing revenue with occupancy. Forbytes worked closely with Guesty’s data science and product teams to create a flexible algorithm. We trained the model on real booking data so it can offer pricing that fits each situation.
- Seamless integration with other systems. Guesty’s platform must work with many external systems like Airbnb and Booking.com. We designed the pricing engine as a modular microservice. It works independently but connects smoothly with the rest of the Guesty ecosystem.
- Allowing automation without losing control. Users can let the engine handle everything or add custom rules for specific dates, events, or holidays. If needed, they can manually override prices, too.
- Meeting local tax rules and legal regulations. Every region has its own tax rules, minimum price limits, or pricing laws. Forbytes helped localize the engine so it follows local regulations.
- Continuous learning and optimization. We added a feedback loop to the engine. It collects data about performance and then uses this data to improve pricing suggestions over time. The system learns and gets smarter with every booking.
- Building trust through transparency. The dashboard shows why a price was suggested — pointing to things like competitor rates, events, or guest demand. This helps users understand how pricing works and builds trust.
