**AI Hotel Revenue Management System (Price Monitoring + Competitor Analysis + Dynamic Pricing)**
Budget: €250 – €750 EUR
## Project Overview
We are looking for an experienced software development team (or senior full-stack developer) to build a complete **Hotel Revenue Management System (RMS)** for our hostel group.
The system will monitor competitor prices, analyze market demand, predict occupancy, and recommend the best room prices using Artificial Intelligence.
Initially, the platform will manage approximately **6 hostels located in Spain**, with the possibility of expanding in the future.
The goal is to maximize occupancy and revenue by making pricing decisions based on real-time market data.
---
# Main Features
## 1. Competitor Price Monitoring
The system must:
* Monitor prices from approximately **10 competitors for each hostel**.
* Collect room prices from:
* Booking.com
* Expedia
* Hostelworld
* Additional OTAs in future versions
* Compare prices by:
* Date
* Room type
* Cancellation policy
* Occupancy
* Store historical pricing data.
* Detect price increases and decreases.
---
## 2. Daily Automated Data Collection
The platform should automatically collect pricing information every day.
Preferred scheduling:
* Once every morning (around 6:00 AM)
* Optional manual refresh
---
## 3. AI Pricing Recommendations
Using collected data, the system should recommend the optimal selling price for each room.
The recommendation should consider:
* Competitor prices
* Historical occupancy
* Current occupancy
* Booking pace
* Day of week
* Seasonality
* Holidays
* Local events
* Historical prices
* Historical demand
Example:
> "Increase Deluxe Room price from €95 to €112 due to high demand and low competitor availability."
---
## 4. Demand Prediction
The AI should forecast demand for approximately the next **90 days**.
The prediction should estimate:
* Expected occupancy
* Expected demand
* Recommended selling prices
* High-demand periods
* Low-demand periods
---
## 5. Event Detection
The system should automatically identify events that may influence hotel demand, including:
* Concerts
* Festivals
* Conferences
* Trade fairs
* Sports events
* National holidays
* Local holidays
These events should influence pricing recommendations.
---
## 6. Dashboard
Modern responsive dashboard including:
### Overview
* Occupancy
* Average Daily Rate (ADR)
* Revenue Per Available Room (RevPAR)
* Competitor average prices
* Revenue trends
### Competitor Analysis
For each hostel:
* Current selling price
* Competitor prices
* Market average
* Recommended price
* Difference vs competitors
### Historical Charts
Interactive charts showing:
* Price evolution
* Occupancy trends
* Revenue
* Competitor pricing
* Demand
---
## 7. Alerts
Automatic alerts when:
* Competitors significantly increase prices
* Competitors significantly decrease prices
* Local demand increases
* Local demand decreases
* Rooms are priced below market
* Rooms are priced above market
Notifications should be sent via:
* Email
* WhatsApp (preferred)
---
## 8. Reports
Automatic reports including:
Daily Report
* Market overview
* Recommended actions
* Price changes
* Competitor changes
Weekly Report
* Occupancy summary
* Revenue summary
* Best-performing properties
* Pricing opportunities
---
## 9. Multi-Property Support
The platform must support multiple hotels/hostels.
Initially:
Approximately **14 properties**
Each property should have:
* Individual competitors
* Individual pricing rules
* Individual dashboard
---
## 10. Historical Database
Store all collected data including:
* Prices
* Competitors
* Occupancy
* Revenue
* Recommendations
* Market trends
This information will be used for AI learning.
---
# Technology Preferences
Preferred technologies:
Backend
* Python
* FastAPI
Frontend
* React
* Next.js
Database
* PostgreSQL
Scraping
* Playwright (preferred)
* Selenium (acceptable if justified)
Deployment
* Docker
* Docker Compose
* Nginx
* SSL
Version Control
* GitHub
---
# AI
The developer may use:
* OpenAI
* Anthropic
* Local LLMs
* Machine Learning models
The objective is to build an intelligent pricing recommendation engine.
---
# Future Integrations
The architecture should be designed for future integrations with:
* Property Management Systems (PMS)
* Channel Managers
* Booking APIs
* Expedia APIs
* Stripe
* Google Analytics
---
# Deliverables
The project must include:
* Complete source code
* Installation guide
* Docker deployment
* Database schema
* API documentation
* Administrator dashboard
* User management
* Documentation
* Testing
---
# Ideal Candidate
We are looking for developers with experience in:
* Hotel Revenue Management
* Dynamic Pricing
* AI
* Web Scraping
* Python
* FastAPI
* React
* PostgreSQL
* Docker
* Machine Learning
Please include examples of similar projects.
---
# Proposal Requirements
When submitting your proposal, please include:
* Estimated budget
* Estimated timeline
* Similar projects
* Recommended technology stack
* Team size
* Experience with hotel pricing or revenue management systems
We are looking for a long-term development partner, not just a one-time freelancer. The platform will continue evolving with new AI features and integrations after the initial release.
We are looking for an experienced software development team (or senior full-stack developer) to build a complete **Hotel Revenue Management System (RMS)** for our hostel group.
The system will monitor competitor prices, analyze market demand, predict occupancy, and recommend the best room prices using Artificial Intelligence.
Initially, the platform will manage approximately **6 hostels located in Spain**, with the possibility of expanding in the future.
The goal is to maximize occupancy and revenue by making pricing decisions based on real-time market data.
---
# Main Features
## 1. Competitor Price Monitoring
The system must:
* Monitor prices from approximately **10 competitors for each hostel**.
* Collect room prices from:
* Booking.com
* Expedia
* Hostelworld
* Additional OTAs in future versions
* Compare prices by:
* Date
* Room type
* Cancellation policy
* Occupancy
* Store historical pricing data.
* Detect price increases and decreases.
---
## 2. Daily Automated Data Collection
The platform should automatically collect pricing information every day.
Preferred scheduling:
* Once every morning (around 6:00 AM)
* Optional manual refresh
---
## 3. AI Pricing Recommendations
Using collected data, the system should recommend the optimal selling price for each room.
The recommendation should consider:
* Competitor prices
* Historical occupancy
* Current occupancy
* Booking pace
* Day of week
* Seasonality
* Holidays
* Local events
* Historical prices
* Historical demand
Example:
> "Increase Deluxe Room price from €95 to €112 due to high demand and low competitor availability."
---
## 4. Demand Prediction
The AI should forecast demand for approximately the next **90 days**.
The prediction should estimate:
* Expected occupancy
* Expected demand
* Recommended selling prices
* High-demand periods
* Low-demand periods
---
## 5. Event Detection
The system should automatically identify events that may influence hotel demand, including:
* Concerts
* Festivals
* Conferences
* Trade fairs
* Sports events
* National holidays
* Local holidays
These events should influence pricing recommendations.
---
## 6. Dashboard
Modern responsive dashboard including:
### Overview
* Occupancy
* Average Daily Rate (ADR)
* Revenue Per Available Room (RevPAR)
* Competitor average prices
* Revenue trends
### Competitor Analysis
For each hostel:
* Current selling price
* Competitor prices
* Market average
* Recommended price
* Difference vs competitors
### Historical Charts
Interactive charts showing:
* Price evolution
* Occupancy trends
* Revenue
* Competitor pricing
* Demand
---
## 7. Alerts
Automatic alerts when:
* Competitors significantly increase prices
* Competitors significantly decrease prices
* Local demand increases
* Local demand decreases
* Rooms are priced below market
* Rooms are priced above market
Notifications should be sent via:
* WhatsApp (preferred)
---
## 8. Reports
Automatic reports including:
Daily Report
* Market overview
* Recommended actions
* Price changes
* Competitor changes
Weekly Report
* Occupancy summary
* Revenue summary
* Best-performing properties
* Pricing opportunities
---
## 9. Multi-Property Support
The platform must support multiple hotels/hostels.
Initially:
Approximately **14 properties**
Each property should have:
* Individual competitors
* Individual pricing rules
* Individual dashboard
---
## 10. Historical Database
Store all collected data including:
* Prices
* Competitors
* Occupancy
* Revenue
* Recommendations
* Market trends
This information will be used for AI learning.
---
# Technology Preferences
Preferred technologies:
Backend
* Python
* FastAPI
Frontend
* React
* Next.js
Database
* PostgreSQL
Scraping
* Playwright (preferred)
* Selenium (acceptable if justified)
Deployment
* Docker
* Docker Compose
* Nginx
* SSL
Version Control
* GitHub
---
# AI
The developer may use:
* OpenAI
* Anthropic
* Local LLMs
* Machine Learning models
The objective is to build an intelligent pricing recommendation engine.
---
# Future Integrations
The architecture should be designed for future integrations with:
* Property Management Systems (PMS)
* Channel Managers
* Booking APIs
* Expedia APIs
* Stripe
* Google Analytics
---
# Deliverables
The project must include:
* Complete source code
* Installation guide
* Docker deployment
* Database schema
* API documentation
* Administrator dashboard
* User management
* Documentation
* Testing
---
# Ideal Candidate
We are looking for developers with experience in:
* Hotel Revenue Management
* Dynamic Pricing
* AI
* Web Scraping
* Python
* FastAPI
* React
* PostgreSQL
* Docker
* Machine Learning
Please include examples of similar projects.
---
# Proposal Requirements
When submitting your proposal, please include:
* Estimated budget
* Estimated timeline
* Similar projects
* Recommended technology stack
* Team size
* Experience with hotel pricing or revenue management systems
We are looking for a long-term development partner, not just a one-time freelancer. The platform will continue evolving with new AI features and integrations after the initial release.