Systematic Study Of Real Estate Valuation Using A Machine Learning Model

Systematic Study Of Real Estate Valuation Using A Machine Learning Model

Registration ID: IJNRD_187124

Published ID: IJNRD2302055

DOI: Click Here to Get

Devi Priya Pulivarthy , Kamma Udaya Lakshmi , Bhavana Govvala , Dasari Tarani

Hedonic price model, Multiple liner regression, Artificial neural network, Support vector regression, XG boost.

Machine learning is widely used in real estate valuation to predict the price of house with high accuracy. Real estate valuation is a decision model in which number of attributes such as structural attributes and locational attributes are taken to predict the house price. The fluctuation of prices in real estate market are always a concern for both the land buyers and seller. So, literature survey has been done to analyse the most efficient attributes and models to predict the accurate house price. In the analysing process there are many types of models to predict the house price like hedonic price model, multiple liner regression, artificial neural network, support vector regression, XG boost etc… This study will help the future researches and housing agents to select the most significant machine learning model for accurate prediction of house pricing.

Published Paper   E-Certificate

"Systematic Study Of Real Estate Valuation Using A Machine Learning Model", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 2, page no.a442-a448, February-2023, Available :https://ijnrd.org/papers/IJNRD2302055.pdf

Volume 8 Issue 2, February-2023

Pages : a442-a448

Paper Reg. ID: IJNRD_187124

Published Paper Id: IJNRD2302055

Downloads: 000121195

Research Area: Computer Science & Technology 

Country: Vadodara, Gujarat, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2302055.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2302055

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

IJNRD is Scholarly open access journals, Peer-reviewed, and Refereed Journals, High Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool), Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) with Open-Access Publications.

INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

Indexing In Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar | AI-Powered Research Tool, Microsoft Academic, Academia.edu, arXiv.org, Research Gate, CiteSeerX, ResearcherID Thomson Reuters, Mendeley : reference manager, DocStoc, ISSUU, Scribd, and many more

How to submit the paper?

Important Dates for Current issue

Paper Submission Open For: August 2025

Current Issue: Volume 10 | Issue 8

Last Date for Paper Submission: Till 31-Aug-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: International Peer-reviewed, Refereed, and Open Access Journal.

Subject Category: Research Area