International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net
MERNest (Real Estate Portfolio Website using MERN Stack)
Swayam Das1, Raghav Bhutra2, Kaushik Dhara3, Aman Keshri4 , Divya Gajangi5, Diksha Hurkat6
1,2,3,4Students, Dept. of Information Technology, Atharva College of Engineering, Maharashtra, India 5,6Professors, Dept. of Information Technology, Atharva College of Engineering, Maharashtra, India
Abstract The goal of this project is to create a comprehensive website that will allow a real estate company to effectively present its portfolio, draw in fresh clients, and foster enduring relationships with its current client base. This project aims to deliver a scalable and efficient platform for real estate businesses to showcase their portfolios online. The website will function as an online gallery showcasing the goals, initiatives, and accomplishments of the business. Both new and existing customers will have a smooth experience with the website because of its intuitive navigation, eyecatching design, and educational material. The project highlights the value of having a strong online presence in the real estate sector and how it can boost client happiness and business expansion. The website is designed to be easy to use, visually appealing, and works well on different devices like phones and computers. It provides a simple and efficient solution for real estate businesses to showcase their properties to potential buyers or renters. Key features include property search functionality, detailed property listings, user authentication, and an administrative dashboard for managing portfolios. The application enables users to filter properties based on location, price, and other specifications. The backend is built on Node.js and Express to handle API requests and MongoDB is used to store and manage property data and user information. React provides a smooth and responsive front-end user experience.
Keywords: digital marketing,portfolio,real estate,user experience,websitecreation.
1. INTRODUCTION
The real estate market is increasingly relying on digital platforms to streamline property management, enhance user experience, and improve accessibility for investors, buyers, and property managers. This Real Estate Portfolio Website, developed using the MERN stack (MongoDB, Express, React, Node.js), aims to provide a comprehensive, user-friendly platform for managing real estate portfolios [3] By integrating modern web technologies, this project offers a seamless, responsive interface for users to search, list, and manage properties, making it a valuable tool for individualsandbusinessesintherealestatesector[1].
The platform is designed with multiple features to cater to different user roles, such as buyers, property owners, andadministrators.Userscanbrowsethroughan
extensive range of properties, apply filters, view detailed propertydescriptions,andinteractwithimagesandmaps [4]. The website also provides secure login functionality and personalized property management, ensuring that each user has access to a tailored experience based on theirrole[6]
Built on the robust MERN stack, the website leverages MongoDBforitsNoSQLdatabase,providingflexibilityand scalability for managing large amounts of data. Express and Node.js work seamlessly to handle the backend servicesandAPIrequests,ensuringsmoothdataflowand efficient operations [10]. React is used to create an engaging, fast, and dynamic front-end experience for users, making the platform not only functional but also visuallyappealing[10]
This project aims to enhance the real estate transaction process by providing a streamlined digital solution that addresses both user needs and business objectives, combining the power of modern web technologies with theintricaciesofrealestatemanagement[1]
2.NEED AND MOTIVATION
The rapid digital transformation of the real estate industryhasmadeonlineplatformsessentialforproperty management, buying, and selling. Traditional real estate processes often lack efficiency, transparency, and user engagement, creating a need for a modern, technologydriven solution [3], [8]. MERNest aims to bridge this gap by offering an interactive and AI-powered real estate portfolio website that enhances property discovery, user experience, and business operations [4] The motivation behind choosing the MERN stack (MongoDB, Express.js, React.js, Node.js) lies in its scalability, flexibility, and efficiencyfordevelopingmodernwebapplications.
Additionally, integrating an AI-powered chatbot enables real-time assistance, property recommendations, and appointment scheduling, improving user interaction [6], [10]. By leveraging cutting-edge technologies such as virtual tours, interactive maps, and data-driven insights, MERNest ensures a seamless and engaging property exploration experience [9]. This project not only enhances the visibility and digital presence of real estate businesses but also simplifies operations, making property managementmoreefficientanduser-friendly[1].
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
3.LITERATURE REVIEW
[1] Dhairyashil Patil, Yashwant More, and Vallabh More (2024) proposed a system for real estate websites that highlights their importance in connecting buyers and renters.
[2] Amit Kumar, Maninder Singh, Sahil Dutta, and Dr. Jasleen Kaur (2024) proposed a Real Estate Web Application leveraging cloud computing, AI, and data analytics for efficient property management. Their system integrates intelligent listings, tenant management, and financial tracking to enhance user experience. This aligns with MERNest, which incorporates AI-powered chatbots and advanced property listings to streamline real estate transactions and improve engagement through modern web technologies.
[3] Yogesh Kadam et al. (2023) compared the MERN stack with previous web development technologies such as HTML, CSS, SQL, and NoSQL. Their research highlights MERN’sadvantages,includingscalability,real-timedata processing, and full-stack JavaScript compatibility. This reinforcesMERNest’schoiceofMERNstackforaflexible andhigh-performancerealestateplatform.
[4] Karen Bayne, Tony Garrett, and Sarah Todd (2022) studied the Technology Acceptance Model (TAM) for real estate search engines. They found that perceived usefulness, ease of use, and enjoyment significantly impact user adoption of online property search platforms. This validates MERNest's emphasis on an intuitive UI/UX, advanced filtering options, and chatbot assistancetoimprovecustomerengagement.
[5] Gupta, Hathwar, and Vijayakumar (2024) provided insights into AI-driven chatbots and their role in enhancing digital interactions. Their study highlights Natural Language Processing (NLP) techniques that allow chatbots to understand user intent and provide context-aware responses. This supports MERNest’s integration of an AI-powered chatbot to assist users in property searches, inquiries, and appointment scheduling,ensuringamorepersonalizedexperience.
[6] Joshi (2024) conducted an analysis of the MERN stack's performance and scalability, identifying key advantages such as high modularity, maintainable code, and real-time interactivity. The study also highlights challenges such as SEO optimization for SPAsandtheJavaScriptlearningcurve.Thesefindings support MERNest’s approach to optimizing performance while maintaining a seamless user experience.
[7] Chowdary et al. (2023) provided a comparative analysis of MERN stack versus other frameworks like Django, LAMP, and Spring Boot. Their study emphasizes MERN’s efficiency for modern web applications due to real-time updates, modular components, and scalability. MERNest leverages these strengths to ensure a responsive and interactive real estateexperience.
[8] Goyal et al. (2023) compared MERN stack with
traditional technologies like PHP, SQL, and MEAN. Their study reveals that MERN enables faster developmentcycles,betterfront-endinteractions,and seamless API integration. This justifies MERNest’s decisiontoadopt MERNoverlegacyframeworks fora modern,user-friendlyrealestateportfoliowebsite.
[9] Arvind Nair et al. (2024) created a platform designed tocatertodiverserealestateneedsinIndia,including property buying, selling, and leasing. It serves as a valuable tool for investment by maintaining a comprehensive database of various properties and agentinformation.
[10] Vaishnavi Singh (2024) proposed a real estate application built using the MERN (MongoDB, Express.js,React,Node.js)stackcouldfocusonseveral key elements. It might include a brief overview of the application's purpose, such as facilitating property listings, user interactions, and transactions. Additionally, it could mention the technologies used, their roles in the app, and how they come together to createaseamlessrealestateplatform.
4.METHODOLOGY
4.1 Aim
The main aim of the real estate portfolio website is to createauser-friendlyandefficientplatformfordisplaying property listings. The website will allow buyers, sellers, andagentstoeasilybrowse,view,andmanageproperties in one place, providing a smooth experience for all users [1],[9].Thisplatformwillshowcaserealestateproperties with detailed information, interactive maps, and user engagement features, providing both buyers and sellers with an efficient and dynamic experience. The platform will be built using the MERN stack (MongoDB, Express, React, Node.js) to ensure scalability, flexibility, and responsiveness[10].
4.2 Objective
Theprimaryobjectiveoftherealestateportfoliowebsite usingtheMERNstackistoprovideacentralizedplatform for users to efficiently manage, showcase, and track real estate properties. The website aims to offer a userfriendly and responsive interface built with React, ensuringseamlessnavigationandanengagingexperience forbothpropertyownersandpotentialbuyers.
To enhance usability, the platform will feature advanced search and filtering options, allowing users to find properties based on location, price range, property type, and availability. MongoDB will be used as the database to storepropertydetailsefficiently,ensuringfastretrievaland scalability. Additionally, the platform will incorporate
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
GoogleMapsAPIforinteractivepropertylocationsearches, improving user engagement and accessibility Looking ahead, future enhancements will include AI-driven property recommendations and market trend analysis, helping users make informed real estate decisions. By achieving these objectives, the platform will serve as a comprehensive, scalable, and efficient solution for real estateportfoliomanagement.
4.3Implementation
The implementation of the real estate portfolio website using the MERN stack followed a structured approach, ensuring efficiency, scalability, and a seamless user experience [3]. The development process began with the selection of the technology stack, leveraging MongoDB as the database for storing property details and user information, Express.js for building a robust backend, React.js for creating a dynamic and responsive user interface, and Node.js for handling server-side operations. These technologies were chosen for their flexibility and ability to work cohesively in a full-stack JavaScript environment[10].
The database was designed using MongoDB, with collections created for users, properties, transactions, and inquiries. Mongoose was utilized as the Object-Document Mapping tool to define schemas and manage interactions with the database efficiently [6]. The backend was developed using Node.js and Express.js, implementing RESTful APIs for handling user authentication, property management, search functionality, and image uploads [2]. Inclusion of Chatbot and 360-degree camera view for enhancing user experience [5] On the frontend, React.js was used to build an intuitive and user-friendly interface with a focus on responsiveness. The property listing pages featured advanced filtering and search capabilities, allowinguserstobrowseandmanagelistings[2].
Google Maps API was integrated to provide interactive location-based search, enhancing the overall user experience. Styling and UI components were developed using Material UI and Tailwind CSS, ensuring a modern and visually appealing design. The frontend and backend communicated via API endpoints, enabling seamless data retrievalandupdates[3].
Looking ahead, future enhancements will focus on incorporating AI-driven property recommendations, blockchain-basedsmartcontractsforsecuretransactions, and predictive analytics for market trends. These improvements aim to provide users with a more intelligentanddata-drivenrealestateexperience[5].The successful implementation of the real estate portfolio website using the MERN stack resulted in a feature-rich, scalable, and secure platform that streamlines property management and facilitates seamless interactions betweenbuyers,sellers,andinvestors[3],[10].
4.4 System Requirements
HARDWAREREQUIREMENTS:
Dual-core or better (Intel i5 or equivalent recommended)
Minimum 8 GB (16 GB recommended for smoothmultitasking)
20 GB free disk space for project files, databases,andtools
Required for installing dependencies, testing, anddeploying
SOFTWAREREQUIREMENTS:
VSCode,Git,GitHub,Postman,npm/Yarn.
React.js, Redux/Context API, Tailwind CSS, ReactRouter.
Node.js, Express.js, Mongoose, Multer, WebSockets.
MongoDB,MongoDBAtlas,Mongoose,Indexing& AggregationPipelines.
5.RESULTS
The development of the real estate portfolio website using theMERNstacksuccessfullymetitsobjectives,deliveringa scalable, efficient, and user-friendly platform for managing real estate properties. The system enables seamless property management, allowing users to add, update, delete, and browse listings with detailed descriptions, images, and pricing. With MongoDB as the database, data storageandretrievalareoptimizedforhandlinglarge-scale propertylistings.
ThewebsiteoffersamodernandresponsiveUIbuiltwith React, ensuring smooth navigation and accessibility across different devices. Users benefit from advanced search and filtering features, enabling them to find properties based on location, price range, and property type. Additionally, real-time notifications powered by WebSockets keep users informed about new property listings, inquiries, and status updates, while automated email notifications provide further engagement. The integration of Google Maps API allows for interactive location-based property searches, enhancing the overall userexperience.
6.CONCLUSION
In conclusion, developing a real estate portfolio website usingtheMERN stack isa practical andeffectivesolution for meeting the needs of buyers, sellers, and real estate agents. The MERN stack provides a robust framework that allows for a responsive and user-friendly platform, enabling users to easily search for and view property listings [1], [10]. The implementation plan outlines clear steps, from planning and design to development and deployment, ensuring a structured approach to building thewebsite.Byfocusingonuserexperience,security,and scalability, the website can effectively compete with larger platforms while providing tailored solutions for smaller real estate businesses [1], [9]. As the real estate marketcontinuestoshiftonline,thiswebsitewillnotonly help users find properties more easily but also empower agentsandagenciestoshowcasetheirlistingseffectively. Overall, the project has the potential to enhance the real estateexperienceforeveryoneinvolved[2].
REFERENCES
[1] Patil, D., More, Y., & More, V. (2024). Real Estate Website. International Research Journal of ModernizationinEngineeringTechnologyandScience (IRJMETS).
[2] Kumar, A., Singh, M., Dutta, S., & Kaur, J. (2024). Development and Implementation of a 'Real Estate Web Application' for Modern Web Application. International Journal for Research in Applied Science andEngineeringTechnology(IJRASET).
[3] Kadam,Y.,Goplani,A.,Mattoo,S.,Gupta,S.,Amrutkar,D., & Dhanke, J. (2023). Introduction to MERN Stack & Comparison with Previous Technologies. European ChemicalBulletin,12(SpecialIssue),14382-14386.
[4] Bayne, K., Garrett, T., & Todd, S. (2022). Consumer Search & Real Estate Websites: A Replication and ExtensionoftheTAM ResearchGate
Fig-3:
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
[5] Gupta, A., Hathwar, D., & Vijayakumar A. (2024). Introduction to AI Chatbots International Journal of Engineering Research & Technology (IJERT).
[6] Joshi A. (2024). Pros and Cons Analysis of MERN Stack Framework for Web Development International Journal of Research Publication and Reviews (IJRPR), Vol (5), Issue (5), Pages 5638-5642
[7] Chowdary, N. R., Dammala, V. G., Donthineni, M., & Pathan, S. K. (2023). Exploring MERN Stack and Tech Stacks: A Comparative Analysis International Research Journal of Engineering and Technology (IRJET), Vol (10), Issue (12).
[8] Goyal, V., Mishra, A. K., & Singh, D. (2023). Implementation and Comparison of MERN Stack Technology with HTML/CSS, SQL, PHP & MEAN in Web Development. International Research Journal of Modernization in Engineering, Technology and Science (IRJMETS), Vol (5), Issue (11).
[9] Arvind Nair, Aryan Sadvelkar, Sarvesh Damle and Neha Kudu. (2024). Revolutionizing Student Accommodation: A MERN-Based Rental System for SeamlessPropertyManagement International Journal for Multidisciplinary Research (IJFMR), Volume (6), Issue (3).
[10]Vaishnavi Singh. (2024). The Real Estate Application using MERN Stack. International Journal of Scientific Research in Engineering and Management (IJSREM). Volume (8), Issue (5).