AI-Powered Flat Finder: A Real Estate Search Assistant using Gemini, React, and Firebase
Bibliographic record
Abstract
ABSTRACT — The real estate industry is increasingly leveraging artificial intelligence to enhance property discovery and decision-making. This paper presents a web-based application that serves as an AI-powered flat-finding assistant. Built using ReactJS for the frontend, Gemini (Google’s generative AI) for conversational intelligence, and Firebase for backend and database management, the application enables users to interact with a chatbot to find flats matching their preferences. The system allows users to ask natural language questions, which are interpreted by Gemini AI, and matched with property listings stored in Firebase. A custom training layer is added to Gemini to ensure relevant and consistent answers based on predefined intents. The paper details the architecture, data flow, and interaction design of the platform, emphasizing real-time communication and personalized responses. This work illustrates how AI-driven interfaces can modernize property searches, improve client engagement, and streamline real estate operations. Keywords — Real Estate, Artificial Intelligence, Gemini AI, Firebase, ReactJS, Flat Finder, Property Recommendation, Chatbot.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".