Selecting suitable land can be a challenging process because market information is scattered across multiple sources and presented in inconsistent formats. Many users rely on manual research, informal advice, and personal judgment when evaluating land, which makes the process time-consuming, less structured, and difficult to compare across districts. Robou is a bilingual web-based land market intelligence and district recommendation platform designed to support smarter land selection in the Eastern Province of Saudi Arabia, specifically Dammam, Khobar, and Dhahran. The platform integrates market data, geographic indicators, interactive maps, and machine learning to provide organized, location-based insights within a single system. Users can select an area directly on the map, choose land type, and define preferred proximity to services. Based on these inputs, Robou evaluates districts and returns ranked recommendations supported by district-level market data and predictive analysis. The system also supports secure sign-in and personalized features such as saving favorite districts for later review. The final machine learning model uses CatBoostRegressor, where the best performance was achieved when the minimum number of deals was set to 10. The model achieved a test R² of 0.645, MAE of 359.90 SR/m², and MAPE of 20.77%, showing improved ability to learn district-level price patterns and support the recommendation process.

Robou land market intelligence district recommendation CatBoostRegressor GIS Eastern Province Saudi Arabia real estate analytics