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Journal articles on the topic 'House Price Prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Vijay Bhasker Reddy, V., Burugu Chandana, Reddy Sindhu Reddy, Tandava Charitha, and Sravya Siripragada. "HOUSE PRICE PREDICTION." YMER Digital 21, no. 05 (2022): 762–67. http://dx.doi.org/10.37896/ymer21.05/87.

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Because house prices rise every year, a mechanism to forecast future house values is required. House price prediction can help the developer determine the selling price of a house and can help the customer to arrange the right time to purchase a house. Physical conditions, concept, and location are only a few of the aspects that determine the price of a home. Usually, House price index represents the summarized price changes of residential housing. While for a singlefamily house price prediction, it needs a more accurate method based on location, house type, size, build year, local amenities,

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Sharma, Vivek. "House Price Prediction Website." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35291.

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Abstract The project focuses on developing a user-friendly web platform that predicts house prices based on various factors like location, size and historical pricing trends.This project not only demonstrates technical skills but also addresses a real-world problem. This project shows us that the machine learning algorithm based on accuracy, consistency out performs the other in the performance of the housing price prediction. Python is used for writing the Machine Learning Algorithms .We will implement a linear regression algorithm on our dataset. HTML,CSS and JS is used for designing the fro

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Juneja, Dr Sonia. "House Price Prediction Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3156–64. http://dx.doi.org/10.22214/ijraset.2023.54259.

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Abstract: 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 i

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VH, Gangadharayya, Abhishek DC, Naveen NB, and Dr Md Irshad Hussain B. "House Price Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem36791.

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Predicting house prices is an important research and application area in the fields of real estate economics and social sciences. This study uses statistics that include various characteristics of houses, such as location, size, age and quality, to create a method for estimating house prices. Accurate predictions are achieved through powerful data processing, feature selection and modeling techniques, including background analysis and machine learning algorithms. The results show that factors such as location, size, and neighborhood characteristics have a significant impact on home prices. Add

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Kong, Jiajin. "House price prediction." Applied and Computational Engineering 75, no. 1 (2024): 141–46. http://dx.doi.org/10.54254/2755-2721/75/20240526.

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In recent years, more and more facts have proven that neural network models have good performance in machine learning, and researchers always use RNN and CNN models to solve complex problems, like how RNN can utilize historical information to predict further weather, identify human notes, and analyze text. The CNN model is always used to identify signatures, process picture images, and classify items. How I find that in the linear prediction field, people always use linear regression to predict results. For example, in house price prediction, people will use linear regression or the random for

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K, Mahalakshmi, Dharish Jaya priyan J, DharshanRaj N, and Aravind A. "Enhancing House Price Predictability: A Comprehensive Analysis of Machine Learning Techniques for Real Estate and Policy Decision-Making." Data Analytics and Artificial Intelligence 4, no. 2 (2024): 68–75. http://dx.doi.org/10.46632/daai/4/2/11.

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Accurate house price prediction is crucial for stakeholders in real estate markets and economic policy formulation. This research investigates the application of sophisticated machine learning (ML) algorithms to improve the precision of house price forecasting. By analyzing existing literature, we explore the methodologies employed in house price prediction using ML approaches. We emphasize the significance of precise predictions for various stakeholders, including homebuyers, sellers, investors, and policymakers. Additionally, this abstract critically evaluates the strengths and limitations o

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Zou, Chengke. "The House Price Prediction Using Machine Learning Algorithm: The Case of Jinan, China." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 327–33. http://dx.doi.org/10.54097/hset.v39i.6549.

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House prices increase substantially in China from 1998. Because of expensive house prices, most Chinese people have only one chance to select suitable houses. Therefore, building a house price prediction model based on housing conditions is significant for customers to make decisions. This paper collects the estate market data of Jinan city from the HomeLink website and performs several feature selection algorithms to get critical features for house price prediction. The paper compares the classical machine learning methods for the problem, including Multiple Linear Regression, Random Forest,

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Basysyar, Fadhil M., and Gifthera Dwilestari. "House Price Prediction Using Exploratory Data Analysis and Machine Learning with Feature Selection." Acadlore Transactions on AI and Machine Learning 1, no. 1 (2022): 11–21. http://dx.doi.org/10.56578/ataiml010103.

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In many real-world applications, it is more realistic to predict a price range than to forecast a single value. When the goal is to identify a range of prices, price prediction becomes a classification problem. The House Price Index is a typical instrument for estimating house price discrepancies. This repeat sale index analyzes the mean price variation in repeat sales or refinancing of the same assets. Since it depends on all transactions, the House Price Index is poor at projecting the price of a single house. To forecast house prices effectively, this study investigates the exploratory data

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Wijaya, I. Putu Teddy Dharma, and Ida Bagus Dwidasmara. "Uji Performansi Algoritma Linear Regression dan Random Forest Regression pada Implementasi Sistem Prediksi Harga Rumah." Jurnal Nasional Teknologi Informasi dan Aplikasnya 1, no. 3 (2023): 917. https://doi.org/10.24843/jnatia.2023.v01.i03.p18.

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Currently the house has become one of the needs that must be met. The price of a house is the main parameter that determines whether a person or organization buys or invests. In general, house prices are influenced by several factors, including building area, land area, number of bedrooms, number of bathrooms and number of garages. Currently, there are many websites devoted to providing information about buying and selling houses. This of course makes it easier for someone when looking for a house with the desired specifications without the need to come directly to the location. However, the h

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Li, Hanwen. "House price prediction based on machine learning." Applied and Computational Engineering 4, no. 1 (2023): 623–28. http://dx.doi.org/10.54254/2755-2721/4/2023362.

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Machine learning is commonly used in the real estate market. It is vital to apply the idea of machine learning in this field to predict house prices based on various features. The paper will focus on how to use the most appropriate machine learning models for house price prediction. It will use LightGBM(Light Gradient Boosting Machine), Gradient Boosting, and XGBoost(Extreme Gradient Boosting) to train models to predict house prices using the existing data from the Kaggle website. After three models make predictions, they will get an RMSE (root mean square error), whichis0.02975, 0.02537, and

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Bhange, Prof Anup, Rajvaibhav Patil, MD Masoom Khan, Prajwal Pazare, and Harsh Patil. "House Price Prediction." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 2115–16. https://doi.org/10.22214/ijraset.2024.66042.

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Abstract: House price prediction is a critical task in the real estate industry, involving the use of statistical and machine learning techniques to estimate property values based on various features. This study aims to develop a predictive model that utilizes historical data, including attributes such as location, size, number of rooms, property age, and market conditions. By leveraging advanced algorithms like linear regression, decision trees, and neural networks, the model strives to provide accurate and reliable price forecasts. The results of this prediction can benefit homeowners, buyer

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Han, Yueting. "Price Prediction of Ames Housing Through Advanced Regression Techniques." BCP Business & Management 38 (March 2, 2023): 1965–74. http://dx.doi.org/10.54691/bcpbm.v38i.4014.

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House price fluctuates at all times, and it is necessary to predict the future house prices for the reason that it is crucial for buyers, property investors and the economy as a whole. This article reveals the important features relating to house price and demonstrates the method of predicting house prices in Ames, Iowa through advanced regression techniques, such as correlation, feature engineering, and model building. The prediction is performed through machine learning methods based on the dataset containing almost all the features of residential houses in Ames, Iowa. The dataset was origin

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Patekar, Prof Poonam. "House Price Prediction and Recommendation." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 7061–65. http://dx.doi.org/10.22214/ijraset.2023.53273.

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Abstract: Determining how much a house will sell for in a city is still a challenging and time-consuming task. This article's goal is to make predictions about the coherence of non-housing prices. A crucial method to ease the challenging design is to use machine learning, which can intelligently optimize the best pipeline fit for a task or dataset. For individuals who will be residing in a home for an extended period of time but not permanently, it is essential to predict the selling price. Real estate forecasting is a crucial part of the industry. From historical real estate market data, the

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Zhu, Liangji. "Optimization of linear regression in house price prediction." Applied and Computational Engineering 6, no. 1 (2023): 805–12. http://dx.doi.org/10.54254/2755-2721/6/20230928.

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House price prediction plays a very important role in housing transactions. Linear regression based algorithms show good effects in predicting house prices. They have strong interpretability and fast operation speed. However, people ignore the estimation of deviations in linear regression (LR) algorithms. In this paper, k-nearest neighbor (KNN) algorithm is supposed to estimate deviations that are added to the result of linear regression to predict house prices accurately. Furthermore, deviation regression (DR) algorithm is supposed to make the prediction result more accurate. By utilizing Bos

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Huang, Zhishang, and Guanren Lai. "A House Price Prediction Model Based on K-means Clustering and Random Forest in Guangzhou." Frontiers in Business, Economics and Management 10, no. 2 (2023): 377–81. http://dx.doi.org/10.54097/fbem.v10i2.11077.

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This paper addresses the key issues in house price forecasting from multiple perspectives by establishing a house price forecasting model for Guangzhou city, providing valuable information and decision support for home buyers, developers, and the government. First, this paper employs the Person coefficient, stepwise regression model and t-test to address the problem of quantifying data and exploring house price factors. By analyzing the correlation between the relevant variables and house prices, the key characteristics that have significant and strong correlation effects on house prices are o

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Kalyani, Nithya, Bhavani Shankar, Bharat Lekkihal, and Vinod Kumar. "Real Estate Price Prediction Using Artificial Intelligence." International Research Journal of Computer Science 10, no. 05 (2023): 268–72. http://dx.doi.org/10.26562/irjcs.2023.v1005.29.

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The sales of houses are influenced by various factors such as location, area, population, and other relevant information. Predicting individual housing prices can be instrumental in estimating future real estate prices. This study employs the Random Forest algorithm, a machine learning technique, as the primary methodology for developing a housing price prediction model. By focusing on the Random Forest algorithm, this project aims to optimize prediction accuracy and consistency, considering it as the best model for price prediction. The implementation of this project involves using Python (AI

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Yang, Xiangjun. "Research on House Price Prediction based on Machine Learning." ITM Web of Conferences 70 (2025): 02018. https://doi.org/10.1051/itmconf/20257002018.

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Accurately predicting house prices is of vital importance to individual home buyers and investment groups, which not only profoundly affects the formulation of home-buying strategies, but also is closely related to the smooth operation of the economy and the overall development of the society. In recent years, machine learning techniques have shown remarkable potential in house price prediction, as these models can mine the complex nonlinear correlations in large amounts of historical data to produce more detailed and accurate predictions. This study aims to evaluate and compare the performanc

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Sandhya, Kumari, and Sarwar Siddiqui. "House Price Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3714–17. http://dx.doi.org/10.22214/ijraset.2022.43190.

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Abstract: Real estate is the least transparent industry in our ecosystem. Housing prices keep changing day in and day out and sometimes are hyped rather than being based on valuation. Predicting housing prices with real factors is the main crux of our research project. This paper outlines how to predict housing costs using various regression techniques using the Python library. The proposed method takes into account the sophisticated aspects used in the house price calculation and provides a more accurate forecast. This paper uses machine learning to explain how the house price model works and

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Ouyang, Xiaoyan. "House Price Prediction Based on Machine Learning Models." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 870–78. http://dx.doi.org/10.54097/ftyf9665.

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This study aims to provide accurate house price predictions using machine learning algorithms. These predictions can assist decision-makers in making informed property investments and planning. Multiple linear regression and random forest were employed to achieve this goal. First, the acquired data underwent thorough analysis, including preprocessing and visualization. Subsequently, the study employed multiple linear regression and random forest models for house price prediction and evaluated their performance. The multiple linear regression model yielded promising results with an R² score of

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Maloku, Fatbardha. "House Price Prediction Using Machine Learning and Artificial Intelligence." Journal of Artificial Intelligence & Cloud Computing 3, no. 4 (2024): 1–10. http://dx.doi.org/10.47363/jaicc/2024(3)357.

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The escalating annual rise in housing prices introduces volatility and uncertainty into the real estate market, underscoring the critical need for accurate price forecasting systems. Predicting house prices accurately remains challenging due to the multitude of influencing factors. This study aims to identify and analyze key determinants affecting house prices, employing two established machine learning models. Through comparative analysis, the research will recommend the most effective model for enhancing the accuracy of house price predictions.

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Wu, Shenghan. "Shanghai House Price Prediction Using Random Forest." Advances in Economics, Management and Political Sciences 66, no. 1 (2024): 224–30. http://dx.doi.org/10.54254/2754-1169/66/20241234.

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With the increasing complexity of the urban real estate market, accurate prediction of housing prices has become an important task. One of the key applications of machine learning is how to raise and accurately estimate costs. Various factors will affect the price of houses. Most of the current frameworks are all using as detailed features as possible to increase their predicting accuracy. But in real-life conditions, many non-local clients also want to have a clear prediction of the house price. These consumers are not from the area; thus they are unaware of the house's surroundings, includin

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Bai, Yufan. "Predicting the Rise in California Home Prices and Factors Affecting." Transactions on Computer Science and Intelligent Systems Research 7 (November 25, 2024): 291–300. https://doi.org/10.62051/dpz1db11.

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California’s real estate market has been in the spotlight for its unique geography and economic advantages, and home prices have been volatile. Therefore, accurate prediction of house price increases is of great importance to home buyers, investors and policy makers. This paper utilizes the California house price dataset, combines macroeconomic indicators and micro property attributes, and analyzes house price prediction through random forest, decision tree and neural network models. The study results show that the Random Forest model performs the best in predicting house prices with an R²valu

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Dhakne, Dr Amol. "House Price Prediction System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4059–63. http://dx.doi.org/10.22214/ijraset.2023.52578.

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Abstract: When individuals are in the market for a new home, they often display a more cautious approach when it comes to their budgets and overall market strategies. However, the current system for determining house prices typically lacks the essential element of predicting future market trends and potential price increases. The existing system Give the functionality for buyers, allowing them to search for houses by features or address. Machine Learning has significantly contributed to various areas such as natural language processing, product recommendations, healthcare, customer service, an

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Zhang, Yan, Jingru Huang, Jiahui Zhang, Shuying Liu, and Samer Shorman. "Analysis and prediction of second-hand house price based on random forest." Applied Mathematics and Nonlinear Sciences 7, no. 1 (2022): 27–42. http://dx.doi.org/10.2478/amns.2022.1.00052.

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Abstract Using Python language and combined with data analysis and mining technology, the authors capture and clean the housing source data of second-hand houses in Chengdu from Beike Network, and visually analyse the cleaned data. Then, a Random Forest (RF) model is established for 38,363 data elements. According to the visual analysis results, the model variables are revalued, the key factors affecting house prices are studied and the optimised model is used to predict house prices. The experiment shows that the deviation between the house price predicted by the RF model and that predicted b

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Vasudha Bahl and Nidhi Sengar, Manu Shahi, Abhay Singh, Amita Goel. "Machine Learning House Price Prediction." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 186–89. http://dx.doi.org/10.46501/ijmtst061236.

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This document present the implementation of Machine Learning algorithms for the prediction of the house and the real estate prices. As the house and real estate prices are subject to change with the market conditions, so it become very difficult to predict the real estate prices with the conventional methods as it may sometimes gives some exaggerated result that may incur losses. To predict the prices more accurately and precisely we predict the prices based on the statics of that particular area which has all the trends and factors on which the price is dependent. To analyse these data , seve

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Mysore, Sumanth. "Prediction of House Prices Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 1780–85. http://dx.doi.org/10.22214/ijraset.2022.44033.

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Abstract: 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 an

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He, Yuyao. "A Polynomial Linear Prediction Model for Housing Price in the USA." Applied and Computational Engineering 2, no. 1 (2023): 556–62. http://dx.doi.org/10.54254/2755-2721/2/20220593.

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The purpose of this research was to build a linear model for predicting the price of houses. The price of the house could be approximated without knowing the price of every house. In the process of the experiment, the data from real estate markets would be analyzed for the supervised study. A linear model would be utilized to predict the price. Different values of learning rate would be compared, and the most efficient value according to the cost function would be chosen. Finally, the prediction model with learning rate 2 would be chosen and used by people who would like to know the price of h

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Tang, Yiwen. "Hybrid House Price Prediction Model by Integration of Simple Linear Regression and Cubic Spline Interpolation." Theoretical and Natural Science 105, no. 1 (2025): 61–70. https://doi.org/10.54254/2753-8818/2025.22572.

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In this day and age, house-purchase has become a crucial consideration for almost everyone, whether seeking a residence or making an investment. Therefore, analyzing the relationship between these factors and house prices is vitally important for both buyers and sellers to make informed decisions. Some researchers have used a linear regression model that can predict the house price for a company or individual. This paper focuses on a target sample data set of Houses in London from Kaggle. The author firstly provides an analysis of two methods to model the relationship between the dependent var

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Pujitha, A. "A Self-Attention-Driven Deep Learning Framework for House Price Prediction Using Multimodal Data." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41722.

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- House price prediction remains a critical challenge in the real estate domain, requiring the consideration of diverse factors that influence housing prices. Traditional models often fail to capture the intricate relationships between these factors, resulting in limited predictive accuracy. To address this, we propose an end-to-end Joint Self-Attention Mechanism for house price prediction, integrating public facility data (e.g., parks, schools, transit) and satellite imagery to assess environmental contexts. Our approach emphasizes the identification of critical features and their interaction

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Chatwani, Smit, Mayank Chotaliya, and Vinaya Sawant. "House Price Prediction Review." International Journal of Computer Applications 184, no. 7 (2022): 35–39. http://dx.doi.org/10.5120/ijca2022922043.

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Mahajan, Payal, Mayur Gawade, Aniket Patel, Shantanu Barhanpurkar, and Onkar Deshmukh. "House Price Prediction and Recommendation." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2009–13. http://dx.doi.org/10.22214/ijraset.2023.49350.

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Abstract: Data mining is increasingly frequently used in the housing market. In order to estimate house prices, important housing qualities, and many other things, data mining is quite helpful since it can extract pertinent knowledge from raw data. According to research, the real estate market and home owners are frequently concerned about price variations in housing. To analyse the pertinent characteristics and the best models to forecast home values,a literature review is conducted. The analyses' results supported the usage of the K-Nearest Neighbor and Random Forest Regression as the most e

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Sapkal, Kunal. "Machine Learning based Predicting House Prices using Regression Technique." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30682.

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Predicting the price of a house helps for ascertain the house's selling price in a specific area and assist individuals in determining the ideal moment to purchase a home. Our goal in this machine learning task on house price prediction is to use data to develop a machine learning model capable of predicting housing values in the specified area. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to increase the model's efficiency f

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Shukla, Meha Ajay Kumar. "House Price Prediction Using Regression." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (2022): 344–46. http://dx.doi.org/10.22214/ijraset.2022.40272.

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Abstract: The housing sector is the second largest employment provider after agriculture sector in India and is estimated to grow at 30% over the next decade. Housing is one of the major sectors of real estate and is well complemented by the growth of the urban and semi-urban accommodations. Ambiguity among the prices of houses makes it difficult for the buyer to select their dream house. The interest of both buyers and sellers should be satisfied so that they do not overestimate or underestimate price. Our system provides a decisive housing price prediction model to benefit a buyer and seller

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Zhang, Xiaopeng. "Paris House Rental Price Index Prediction-A Classical Statistical Model Approach." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 294–99. http://dx.doi.org/10.54097/q6kz2d72.

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The study focuses on predicting rental prices in Paris and aims to contribute to urban economics and data analytics. It analyzes a wide range of data sources, including historical rental prices, economic indicators, demographics, and regulations. The goal is to compare classical statistical models' prediction accuracy of these three models: ARIMA, dynamic regression, and random forest. The results reveal that the ARIMA model performs best, offering more accurate predictions. ARIMA relies on time series analysis, capturing complex patterns in rental prices, making it well-suited for dynamic rea

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Patel, Deepanshu, Parmeshwar Nayak, Shubham Gupta, and Jayanth C. "House Price Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1870–73. http://dx.doi.org/10.22214/ijraset.2023.53841.

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Abstract: Forecasting the appropriate house pricing for real estate customers while taking into consideration their priorities and financial situation is the goal. By examining previous market patterns, price ranges, and approaching changes, future prices may be predicted. Research indicates that house price discrepancies are a common source of concern for both homeowners and the real estate industry. Several interrelated factors influence the price at which real estate sells in places like Bengaluru. The size, location as well as and amenities of the property are significant considerations th

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Li, Chenxi. "House price prediction using machine learning." Applied and Computational Engineering 53, no. 1 (2024): 225–37. http://dx.doi.org/10.54254/2755-2721/53/20241426.

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The role of the real estate industry in economic development and social progress reflects the economic well-being of individuals and regions. With the increase of people's income level, the demand for housing is also increasing. Therefore, making a more accurate house price forecast will help people make the most correct strategy to buy a house when they need it. This study focuses on house price prediction in King County, Washington, a diverse real estate market. Leveraging machine learning models such as linear regression, random forest, neural networks and XGBoost, these supervised learning

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Chordia, Mr Piyush, Mr Pratik Konde, Ms Supriya Jadhav, Hrutik Pandhare, and Prof Shikha Pachouly. "Prediction of House Price Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (2022): 1387–90. http://dx.doi.org/10.22214/ijraset.2022.40466.

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Abstract: The trend of the sudden drop or constant rising of housing prices has attracted interest from the researcher as well as many other interested people. There have been various research works that use different methods and techniques to address the question of the changing of house prices. This work considers the issue of changing house price as a classification problem and discuss machine learning techniques to predict whether house prices will rise or fall using available data. This work applies various feature selection techniques such as variance influence factor, Information value,

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Chen, Ningyan. "House Price Prediction Model of Zhaoqing City Based on Correlation Analysis and Multiple Linear Regression Analysis." Wireless Communications and Mobile Computing 2022 (May 5, 2022): 1–18. http://dx.doi.org/10.1155/2022/9590704.

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Situated in southern China, Zhaoqing City is a part of Guangdong Province, China. The total administrative area of the city covers 14,891 square kilometers. The data of China’s seventh population census in 2020 showed that the permanent resident population in Zhaoqing City reached up to 4,413,594. Meanwhile, Zhaoqing is one of the cities in the Guangdong-Hong Kong-Macao Greater Bay Area. House price analysis and prediction carried out against Zhaoqing City will have directive significance for relevant policies formulated by the local government, residential investment or purchase of consumers,

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Liu, Guangjie. "Research on Prediction and Analysis of Real Estate Market Based on the Multiple Linear Regression Model." Scientific Programming 2022 (May 9, 2022): 1–8. http://dx.doi.org/10.1155/2022/5750354.

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Since the housing reform in the 1990s, China’s real estate market has expanded and developed rapidly. It has quickly become the pillar of China’s national economy and made a great contribution to China’s economic growth. However, China’s real estate market started late and its development is not perfect, and house prices show obvious volatility. Accurate prediction of house prices is conducive to the government issuing appropriate regulatory policies, helping investors formulate correct investment strategies, and guiding the healthy and long-term development of the real estate market. Based on

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He, Yunling. "Research on the Prediction of US House Prices Based on Machine Learning." BCP Business & Management 32 (November 22, 2022): 385–90. http://dx.doi.org/10.54691/bcpbm.v32i.2956.

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With the increase in the standard, houses have become an indispensable part of people’s lives. However, purchasing houses needs to consider the prices. Therefore, this paper uses machine learning to predict US house prices. Based on the random forest and linear regression method used in this research. It is found that the use of the former has a good effect on predicting multivariate nonlinear relationships in house prices. After comparing the results of actual prices and predicting prices, residents can select different room structures by contrasting them with experimental types as the numeri

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Weng, Weinan. "Research on the House Price Forecast Based on machine learning algorithm." BCP Business & Management 32 (November 22, 2022): 134–47. http://dx.doi.org/10.54691/bcpbm.v32i.2881.

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House price experiences some fluctuations every year, due to some potential factors such as location, area, facilities and so on. Housing price prediction is a significant topic of real estate, and it is beneficial for buyers to make strategy decisions about house dealing. There are many research on house price forecast, yet the current research cannot comprehensively compare and analyze the popular house price prediction approach. Constructing a model begins with pre-processing data to fill null values or remove data outliers and the categorical attribute can be shifted into required attribut

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Priya, G. Gayathri. "House Price Prediction using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3645–50. http://dx.doi.org/10.22214/ijraset.2021.35831.

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The real estate market is one of the most price-driven, but it is still affected by volatility. This is one of the main uses of machine learning ideas to improve and predict costs with high precision. As housing prices are fluctuating, People are cautious when trying to buy a new house based on their budget and marketing strategy. The purpose of the paper is to forecast consistent home prices for non-owners based on their financial dispositions and aspirations. The paper involves predictions using various Regression techniques like linear regression, random forest regression, polynomial regres

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Geerts, Margot, Seppe vanden Broucke, and Jochen De Weerdt. "A Survey of Methods and Input Data Types for House Price Prediction." ISPRS International Journal of Geo-Information 12, no. 5 (2023): 200. http://dx.doi.org/10.3390/ijgi12050200.

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Predicting house prices is a challenging task that many researchers have attempted to address. As accurate house prices allow better informing parties in the real estate market, improving housing policies and real estate appraisal, a comprehensive overview of house price prediction strategies is valuable for both research and society. In this work, we present a systematic literature review in order to provide insights with regard to the data types and modeling approaches that have been utilized in the current body of research. As such, we identified 93 articles published between 1992 and 2021

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Li, Han. "House Price Prediction and Analysis Based on Random Forest and XGBoost Models." Highlights in Business, Economics and Management 21 (December 12, 2023): 934–38. http://dx.doi.org/10.54097/hbem.v21i.14837.

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Accurate prediction of house price is important in housing market. It’s difficult to forecast housing price because it’s influenced by many factors. There has been many discussions on housing price prediction by kinds of machine learning algorithm. This paper attempt to predict housing price by Random Forest and XGBoost models, and compares the performance between them. In this paper, missing values processing, correlation analysis and standardization of samples are carried on the initial data at first, then two machine learning models are constructed, trained and test on the same dataset. The

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Varma, Ayush. "House Price Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32400.

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Real estate is the least transparent industry in our ecosystem. Housing prices keep changing day in and day out and sometimes are hyped rather than being based on valuation. Predicting housing prices with real factors is the main crux of our research project. Here we aim to make our evaluations based on every basic parameter that is considered while determining the price. We use various regression techniques in this pathway, and our results are not sole determination of one technique rather it is the weighted mean of various techniques to give most accurate results. The results proved that thi

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Palupi, Endang. "House Price Prediction Using Data Mining with Linear Regression and Neural Network Algorithms." Jurnal Riset Informatika 6, no. 1 (2023): 15–20. http://dx.doi.org/10.34288/jri.v6i1.262.

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The need for housing in big cities is very high because most offices and economic centers are in big cities. Limited land and high demand cause house prices to rise. Many developers build housing on the outskirts of big cities with access to trains and toll roads to make transportation easier. Property developers compete by providing the best prices, various choices of house specifications, ease of the mortgage process, and attractive promotions such as no down payment. A house is a long-term investment whose price increases yearly, so proper analysis is needed to buy a place to live in. Sever

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Akash Dagar and Shreya Kapoor. "A Comparative Study on House Price Prediction." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 103–7. http://dx.doi.org/10.46501/ijmtst061220.

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Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Due to increase in urbanization, there is an increase in demand for renting houses and purchasing houses. Therefore, to determine a more effective way to calculate house price accurately is the need of the hour. So, an effort has been made to determine the most accurate way of predicting house price by usi

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Kanade, Vaishnavi, and Dr Minakshi Thalor. "HOUSE PRICE PREDICTION USING MACHINE LEARNING." International Journal of Engineering Applied Sciences and Technology 8, no. 4 (2023): 146–48. http://dx.doi.org/10.33564/ijeast.2023.v08i04.019.

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Machine Learning (ML) has profoundly impacted various domains, including speech recognition, healthcare, and automotive safety. Acknowledging its pervasive influence, our project aims to harness ML's capabilities for housing price prediction. In the volatile real estate market, prospective buyers strive to make informed decisions within budget constraints, often hindered by the absence of reliable future market trend forecasts. Our project's primary goal is to provide accurate house price predictions, mitigating potential financial losses. To achieve this, we are developing a housing cost pred

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Liu, Zhihang. "Real Estate Price Prediction based on Supervised Machine Learning Scenarios." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 731–37. http://dx.doi.org/10.54097/hset.v39i.6637.

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House price prediction is one of the most common supervised learning tasks in the machine learning field, which makes it a perfect criterion for the effectiveness of different learning models. From basic regression models to neural networks, countless methods have been proposed to solve the house price prediction problem. In this paper, the focus is the performance of three regression models, linear, LASSO, and ridge. There will be a selected dataset of sold houses from the open-source website. The data will be explored and visualized for a better understanding and then implement the regressio

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Chen, Zuxin. "House Price Prediction in Boston Based on BP Neural Network." Highlights in Science, Engineering and Technology 124 (February 18, 2025): 152–56. https://doi.org/10.54097/2vq1wq81.

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House is a basic demand for living. It is directly related to people's happiness. Affected by many factors such as the economy condition and the policy, house prices are always dynamic. House price prediction can provide decision support for investors, so it is important to optimize the prediction model. Many machine learning methods are applied to house price prediction, such as support vector machine and random forest. This paper predicts the house price of Boston. To solve the multicollinearity problem in the dataset, this paper builds a BP neural network model using Bayesian regularization

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