## 1 Introduction
### Application Showcased in This Paper  
Our system improves upon traditional rental market analysis by dynamically generating micro-regions based on real-time housing listing data rather than relying on fixed administrative boundaries [22]. We employ a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm that automatically groups listings into clusters [23]. Initially, each rental listing is transformed into spatial data points, incorporating geographical coordinates (latitude and longitude) and pricing details [24]. The DBSCAN algorithm then clusters these points using an adaptive approach to calculate the optimal clustering radius (eps) through entropy-based optimization and sets a flexible minimum number of points (minPts) based on the dataset size, ensuring that clusters remain representative and scalable [25]. To further refine these micro-regions, we integrate Open-Source Routing Machine (OSRM 5.27.2) (Heidelberg Institute for Theoretical Studies, Heidelberg, Germany), specifically tailored for pedestrian routing, to measure realistic walking times from each cluster centroid to key points of interest, such as universities. This step significantly enhances practical usability, accounting for real-world variables like terrain, pedestrian pathways, and seasonal variations, resulting in more meaningful micro-region definitions. Empirical validation of this method in recent urban planning research has demonstrated an approximately 5% improvement in accuracy over static boundary approaches, ensuring efficient scaling even for continental-scale applications, with response times maintained at milliseconds. The recommendation engine leverages reinforcement-learning logic to rank listings not only by price and area but also by contextual cues like zone relevance, posting freshness, and text-based similarity to university-specific keywords [26]. This approach sharply reduces irrelevant results and search time, particularly when compared to the keyword-only filtering methods used in most existing platforms [27].  
This implementation is publicly available, cloud-hosted, and fully operational. Both the frontend (React 18.2.0) and backend (Express)S 4.18.2 + MongoDB 7.0.5) are deployed serverless on Vercel (Vercel Inc., San Francisco, CA, USA), offering high availability and cross-device compatibility with no user login required. The application is free and open-source, emphasizing transparency and long-term maintainability. Its architecture is designed to be easily adapted for other cities or extended with additional machine-learning models, enabling reproducibility and future research. By combining live data acquisition, spatial analysis, and student-centered AI recommendations into a single cohesive pipeline, this work sets a new standard for affordability-focused housing tools. It contributes both a practical solution for students in Timisoara and a reusable blueprint for others tackling similar urban housing accessibility problems in academic contexts.  
A critical innovation in our implementation is the integration of an explainable reinforcement-learning (XRL) framework that provides transparent recommendation rationales. Drawing from recent advances in XRL visualization [3], we implemented a multi-layer explanation system that exposes the decision-making process through both visual weight distributions and natural language explanations. The system displays feature importance scores for distance (35%), price alignment (30%), posting freshness (20%), and text similarity (15%) directly in the user interface through intuitive visual indicators. This transparency mechanism has been shown to increase user trust [2].