House Price Prediction Using Machine Learning Via Data Analysis

International Journal for Research in Applied Science and Engineering Technology

House price prediction is the process of using learning based techniques to predict the future sale price of a house. It explores the use of predictive models to accurately forecast house prices. It also examines the effectiveness of using machine learning algorithms to predict house prices. In particular, our research investigates the impact of data such as location, duration of house, dimension of house on the accuracy of the predictions. Finally, a discussion on the implications of using machine learning algorithms for predicting price for consumers and real estate professionals is presented. The proposed method is evaluated using a dataset of real-world housing prices, and results demonstrate that the proposed approach outperforms existing models in terms of both accuracy and robustness. The current research also focusses on potential areas for future research and potential applications of the proposed approach.

Amity Journal of Computational Sciences (AJCS)ISSN: 2456-6616 (Online) 18 www.amity.edu/ajcs , 2020

Machine learning participate a significant role in every single area of technology as per the today's scenario. Even I can Say every phase of our lives is surrounded by the implementation of new era technologies such as Hospitality management, Railway, Transportation, Health care, Industry And so on. Machine learning has been employed for many sectors since past decades like image processing, pattern recognition, medical diagnosis, and predictive analysis, product recommendation. House prices changes every year, so it is mandatory for a structure to foresee house prices in the future. House price prediction can help in fixing and thereby predicting house prices and customer can evaluate it. Our intension is to predict house prices using several machine learning techniques. House price of particular location does depends on various factors like lotsize, bedrooms, bathrooms, location, drawing room, material used in house , interiors, parking area and mainly on square feet per area. Our intension behind proposing this paper is to employ different machine learning techniques for predicting the price based on these metrics. The algorithm used in this analysis is Data refining, OLS regression, Classification, Clustering, correlation matrix.

IRJET, 2022

Buying a house is one of the biggest financial goal of everyone. Owning a house is not only a basic need but it also represents prestige. However, buying a house is one of the most crucial decision of a person's life as there are so many factors to be consider before buying a property. House prices keeps changing based on location, area, population, house condition and structure, availability of parking, backyard, size of house etc. From past few years a lot of data has been generated regarding Real Estate. Machine learning prediction techniques can be very useful to predict an accurate pricing of the houses. The study focuses on developing an accurate prediction model for house price prediction. Machine learning is sub-branch of artificial intelligence that deals with statistical methods, algorithms. Using machine learning we can build a model which can make prediction based on past data. In this paper we will review different machine learning algorithms which can be used for house pricing prediction.

Methods for calculating the sale price of houses in cities remain a difficult and time-consuming task. The purpose of this article is to forecast the coherence of non-house prices. Using Machine Learning, which can intelligently optimize the optimum pipeline fit for a task or dataset, is a key technique to simplify the difficult design. Predicting the resale price of a house on a long-term temporary basis is vital, particularly for those who will be staying for a long time but not permanently. Forecasting house prices is an important aspect of real estate. The literature tries to extract relevant information from historical property market data. The price of real estate causes land price bubbles to expand, causing macroeconomic instability. The reasons that drive up real estate prices are important investigating so that the government may use them as a guide to help stabilize location, and various economic elements influencing at the time are all factors that influence the house selling price.

International Journal of Modern Education and Computer Science

Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field.

Asian Journal of Research in Computer Science

In our ecosystem, real estate is clearly a distinct industry. Predicting house prices, significant housing characteristics, and many other things is made a lot easier by the capacity to extract data from raw data and extract essential information. Daily fluctuations in housing costs are still present, and they occasionally rise without regard to calculations. According to research, changes in property prices frequently have an impact on both homeowners and the real estate market. To analyze the key elements and the best predictive models for home prices, literature research is conducted. The analyses' findings supported the usage of artificial neural networks, support vector regression, and linear regression as the most effective modeling techniques. Our results also imply that real estate agents and geography play important roles in determining property prices. Finding the most crucial factors affecting housing prices and identifying the best machine learning model to utilize f...

Soft Computing, 2021

House price prediction is a significant financial decision for individuals working in the housing market as well as for potential buyers. From investment to buying a house for residence, a person investing in the housing market is interested in the potential gain. This paper presents machine learning algorithms to develop intelligent regressions models for House price prediction. The proposed research methodology consists of four stages, namely Data Collection, Pre Processing the data collected and transforming it to the best format, developing intelligent models using machine learning algorithms, training, testing, and validating the model on house prices of the housing market in the Capital, Islamabad. The data used for model validation and testing is the asking price from online property stores, which provide a reasonable estimate of the city housing market. The prediction model can significantly assist in the prediction of future housing prices in Pakistan. The regression results are encouraging and give promising directions for future prediction work on the collected dataset.

House price forecasting is an important topic of real estate. The literature attempts to drive useful knowledge from historical data of property markets. Machine learning techniques are applied to analyze historical property transaction to discover useful models for house buyers and sellers. Revealed is the high discrepancy between house prices in the most expensive and most affordable suburbs. Moreover, experiments demonstrate that the combination of stepwise and support vector machine that is based on mean squared error measurement is a competitive approach. The goal of the study is through analyzing a real historical transactional dataset to derive valuable insight into the housing market. It seeks useful models to predict the value of a house given a set of its characteristics. Effective model could allow home buyers or real estate agents to make better decisions.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Economy of the country is greatly driven by the prices of houses in that country. Both buyers and sellers depend on the pricing strategies. Ask an emptor to explain the factors they think are considered for pricing the house at that price and that they probably start with railways and end with various attributes. Over here it proves that more factors will be applied on the pricing strategies of the house. The aim of the project is to predict the house prices with various regression models. Nowadays Machine Learning is a booming technology. Data is the heart of Machine Learning. AI and Machine Learning holds the key position in the technological market. All industries are moving towards automation. So we have considered ML as a main predicting subject in our project and worked using it. These days everything fluctuates. Starting with crypto and various business models varies day by day which includes real estate as well so in this project house prediction depends on real estate data and ML techniques. Many people want to buy a good house within the budget. But the disadvantage is that the present system doesn't calculate the house predictions so well and end up in loss of money. So, the goal of our project is to reduce money loss and buy good house. Many factors are there to be considered in order to predict the house price which includes budget factors and fewer house modifications according to the buyer. So, we are considering all of those factors and predicted using various machine learning techniques like SVR, KNN, SGB regression, CatBoost regression, Random forest regression

IJRASET, 2021

With the increase in industrialisation, people are also a lot more careful today when they make an attempt to shop for a brand new house with their budgets and market strategies. Until date, existing websites gift solely the house costs given by the homeowners and details of the house largely infrastructure. Some websites even offer comparison between completely different homes with the same infrastructure. But, some individuals aren't awake to what quantity a house with an exact infrastructure is meant to value and are not ready to find how much is sweet enough to be ready to find frauds. individuals additionally want alternative factors however infrastructure to come to a decision whether or not or to not obtain a house Machine learning algorithmic program helps us in enhancing security alerts, guaranteeing public safety and improve medical enhancements.