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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM48549

AI-Powered Flat Finder: A Real Estate Search Assistant using Gemini, React, and Firebase

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM48549

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM48549

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AS

Ashutosh Shukla

MMA

Md Meraj Alam

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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.

Cite This Article

Ashutosh Shukla, Md Meraj Alam, "AI-Powered Flat Finder: A Real Estate Search Assistant using Gemini, React, and Firebase," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025. https://doi.org/10.55041/IJSREM48549

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Publication Date

5/24/2025

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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