House Price Prediction Research Articles

House Price Prediction Based on Machine Learning Algorithms - Taking Ames as an Example

This study delves into the significance and methods of predicting housing prices. Utilizing a dataset from Kaggle, the author selected 10 variables highly correlated with housing prices, including OverallQual, GrLivArea, and GarageCars. Various models such as random forest and multiple linear regression were employed for prediction and comparison. Results indicate that for data with strong linear relationships, the predictive performance of the multiple linear regression model surpasses that of the random forest model. The paper emphasizes the importance of data preprocessing on model accuracy and suggests that model selection should align with data characteristics and problem requirements. While providing a preliminary exploration of housing price prediction, the study acknowledges shortcomings such as incomplete variable selection and insufficient data processing, suggesting avenues for future research to address these limitations and enhance the predictive capabilities in this domain.

A Novel Framework for Joint Learning of City Region Partition and Representation

The proliferation of multimodal big data in cities provides unprecedented opportunities for modeling and forecasting urban problems, such as crime prediction and house price prediction, through data-driven approaches. A fundamental and critical issue in modeling and forecasting urban problems lies in identifying suitable spatial analysis units, also known as city region partition. Existing works rely on subjective domain knowledge for static partitions, which is general and universal for all tasks. In fact, different tasks may need different city region partitions. To address this issue, we propose JLPR , a task-oriented framework for J oint L earning of region P artition and R epresentation. To make partitions fit tasks, JLPR integrates the region partition into the representation model training and learns region partitions using the supervision signal from the downstream task. We evaluate the framework on two prediction tasks (i.e., crime prediction and housing price prediction) in Chicago. Experiments show that JLPR consistently outperforms state-of-the-art partitioning methods in both tasks, which achieves above 25% and 70% performance improvements in terms of mean absolute error for crime prediction and house price prediction tasks, respectively. Additionally, we meticulously undertake three visualization case studies, which yield profound and illuminating findings from diverse perspectives, demonstrating the remarkable effectiveness and superiority of our approach.

Machine Learning based Predicting House Prices using Regression Technique

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 for this project we are using supervised learning, which is a part of machine learning. We have to go through different attributes of the dataset. This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This suggested model is thought to be the most accurate one for estimating home prices and makes the most accurate predictions. This offers a succinct overview, which is necessary in order to forecast the price of the home. This project consists of what and how the house price model works with the assistance of machine learning technique using scikit-learn and which datasets we will be using in our proposed model. Key Words: house price, lasso regression, ridge regression, R-squared.

Integrating Machine Learning Techniques for Real Estate Analysis

Machine learning can be applied to prediction models. The study delved into house price prediction, an area of significance to both individual participants and policymakers within and beyond the real estate market. The advanced predictive model is a tool that informs quality decision-making. This study synthesizes and builds on existing research to implement and enhance house price prediction methods, assessing the performance of Convolutional Neural Networks (CNNs), Decision Trees, and K-Nearest Neighbors (KNN) which is of focus on Most Correlated Features (KNN-MCF). Although these techniques have been developed and refined over the years, they still face challenges such as overfitting, noises existing in the dataset, and the complexity of modeling both linear and non-linear relationships. By integrating the resources, the study aims to provide objective guides for addressing the common challenges and predicting house prices, illustrating the appropriate analytical methods to apply based on characteristics of a database, extra space for improvements, and critical concerns of their practical applications. Ultimately, the research strives to bridge the gap between theoretical models and real-world applications to develop practical tools for reliable house price prediction.

Study of Influential Factors on Housing Prices by Using MLR

With the emergence of the 2019 pandemic, the global economy experienced a downturn, but the housing market quickly rebounded post-pandemic, heightening the anxiety surrounding home buying. Previous research predominantly employed regression models to forecast housing prices, but these studies largely focused on specific factors. Hence, this article aims to offer a more comprehensive viewpoint by exploring the impact of multiple independent variables on housing prices, particularly emphasizing the effects of the age of buildings, the surrounding environment, and architectural factors. The methodology used in this study is the Multiple Linear Regression (MLR) model. It analyzes the real estate market data in Taipei, meticulously constructing and validating multiple models. The findings reveal that the age of buildings and the distance to the nearest subway station negatively influence housing prices, whereas the number of convenience stores positively impacts them. Among these factors, the quantity of convenience stores exerts the most significant effect on housing prices. Overall, this article provides a novel perspective and tools for understanding and predicting housing prices, assisting real estate developers and buyers in making more informed decisions in the complex real estate market. This study highlights the multidimensional nature of real estate value and contributes to the sustainable development of the real estate market.

Research on the Price Prediction of Commercial Housing in Beijing

In order to determine the best appropriate housing price prediction model, this study constructs a linear regression model, a BP neural network model, and a time series model based on data on commercial housing prices and their affecting variables in Beijing during the past 23 years. A set of 12 independent factors that impact the cost of commercial housing is selected first. Then, three models are created, and their fitting effects are compared and analyzed to forecast the average cost of commercial real estate in Shandong Province. It is shown that the multiple linear regression model is more appropriate for long-term prediction and that the time series model predicts data more recently than the BP neural network model; Therefore, it can be used if the prediction period is longer. Although home prices can be predicted theoretically using these three approaches, more work has to be done to fine-tune the applicable models.

House price prediction using machine learning

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 models are used to delve into house price forecasting. This research includes random forest and XGBoost, are implemented using Scikit-Learn tools. Besides, the Feedforward Neural Network is introduced with the drop out layer in order to reduce the occurrence of model fitting situations. The findings reveal that XGBoost achieves the highest accuracy, making it well-suited for precise price predictions. Additionally, the research identifies grade, sqft_living, and latitude as the three most influential features significantly affecting house prices within the dataset.

House price prediction based on different models of machine learning

Housing price prediction is a typical regression problem in machine learning. Common algorithms include linear regression, support vector regression, random forest, and extreme gradient boosting models based on integrated learning methods. Among the specific problems, different models in the specific problem will get different results. This research will compare these three models to show which model is more accurate and robust. Given the practical problem of housing price prediction, various characteristics of houses are carried out. The research will analyze and study, apply a variety of regression models, and compare the performance of the above three models on this problem, make the horizontal comparison of the advantages and disadvantages of different models, and analyze the difference in effect Line analysis and summary.

Prediction of second-hand house prices in Ames city based on data mining

Prediction of second-hand house prices in Ames city based on data mining

A Comparative Study of Random Forest Regression for Predicting House Prices Using

Based on the rapid development of the real estate market, real estate prices in various regions of the world fluctuate greatly and are unstable, and we need to make some predictions for real estate prices. However, in reality, we pay too much attention to the relationship between past property prices and current property prices and often ignore the prediction of future house prices. Research on predictive models is lacking. Therefore, studying real estate forecasting models is one of the best solutions to solve the problems faced by the real estate market based on the thinking of the current situation. In response to this problem, I propose to use a random forest model, gradient boosting, and optional to build a reasonable predictive model. The final results prove that this predictive model can be used to some extent to predict changing real estate prices in the future market. It is hoped that the method in this paper can provide a reference for subsequent research on predictive models.

Predicting New York Housing Prices: A Machine Learning Approach Incorporating School, Living facilities and Real Estate Market Factors

This study aims to predict housing prices in New York by utilizing machine learning methods that incorporate factors such as schools, living facilities, and the real estate market. This paper collected extensive data, including metrics on school quality, assessments of living facility convenience, and real estate market data. Employing a regression-based machine learning algorithm, the study incorporated these factors into a predictive model. Through training and testing the model, this study discovered that school quantity and the convenience of living facilities significantly impact housing prices. The predictive model demonstrated good accuracy and predictive capability on the test set, validating the effectiveness of this approach. The findings of this study provide valuable insights for real estate market participants, policymakers, and investors to better understand and forecast housing price trends in New York.

Comparison of expert opinion and artificial neural network models in housing price prediction

Bu çalışmada Ankara ili, Yenimahalle ilçesi, Batıkent Bölgesi sınırları içerisinde bulunan konutların öznitelik verileri ve coğrafi konumlarının taşınmaz değerine olan etkileri incelenmiştir. Veri seti düzenlenirken Coğrafi Bilgi Sistemlerinden faydalanılmıştır. Çalışma kapsamında nominal değerleme ve Yapay sinir ağları (YSA) modellemede kullanılmıştır. Bölgedeki taşınmazların elde edilebilecek en yüksek hassasiyet ve en yüksek doğrulukta taşınmaz değer haritaları oluşturularak değer tespitleri yapılmıştır. Modellemede YSA ile daha başarılı piyasa sonuçları tahmin edilmiştir. Nominal değerlemede ise uzman görüşü ile oluşan matematiksel modelin piyasa değerini tahmininin göz ardı edilemeyecek başarı elde ettiğini ve gelecekte modelin güncellemelerle sürdürülebilir olduğu gözlemlenmiştir

House price prediction using machine learning: A case study in Seattle, U.S.

In recent years, predicting housing prices has become a prominent research topic. The motivation behind this research is the lack of precise analysis and comprehensive comparison of different machine learning models for Seattle housing prices. To address this issue, this paper utilizes a dataset obtained from Kaggle, consisting of Seattle housing prices. This study focuses on analyzing and comparing the performance of different machine learning models in predicting housing prices in Seattle. After preprocessing the data, eight different machine learning models are applied to predict housing prices in Seattle. A comprehensive comparison of these models is conducted to analyze their differences. The experimental results show that the differences in performance among the models are not substantial. However, StackingAveragedModels emerges as the top-performing model, with an RMSLE of 0.2328 and an R2 of 0.7771. These findings contribute to a better understanding of the predictive capabilities of different machine learning models for Seattle housing prices.

Dataset Analysis and House Price Prediction

The prediction of house prices through the analysis of data using machine learning and charts is a crucial and significant topic. Many scholars have conducted research in this area, providing valuable insights for both academic learning and real-world applications. The goal of this study is to predict house prices and thoroughly analyze the dataset. The methodology includes data cleaning techniques to ensure data quality. Additionally, three types of charts are employed to analyze the dataset effectively. Finally, two popular models are utilized to predict house prices, and their accuracy is evaluated. The results indicate that the Random Forest Regressor model is more suitable for the specific dataset, and the impact of each factor on house price prediction varies Looking ahead, future research will involve the utilization of more advanced models to further enhance prediction accuracy. This will enable realistic simulations and contribute to the ongoing development of the society. This study has made preliminary progress in data cleaning, dataset analysis, and predictive modeling. The use of charts provides a more intuitive representation of dataset characteristics. The findings have implications for the fields of data cleaning and machine learning.

Comparative analysis of machine learning models in predicting housing prices: a case study of Prishtina's real estate market

Purpose The purpose of this study is to carry out a comparative analysis of four machine learning models such as linear regression, decision trees, k-nearest neighbors and support vector regression in predicting housing prices in Prishtina. Design/methodology/approach Using Python, the models were assessed on a data set of 1,512 property transactions with mean squared error, coefficient of determination, mean absolute error and root mean squared error as metrics. The study also conducts variable importance test. Findings Upon preprocessing and standardization of the data, the models were trained and tested, with the decision tree model producing the best performance. The variable importance test found the distance from central business district and distance to the road leading to central business district as the most relevant drivers of housing prices across all models, with the exception of support vector machine model, which showed minimal importance for all variables. Originality/value To the best of the author’s knowledge, the originality of this research rests in its methodological approach and emphasis on Prishtina's real estate market, which has never been studied in this context, and its findings may be generalizable to comparable transitional economies with booming real estate sector like Kosovo.

House Price Prediction Based on Joint Clustering and Linear Regression

The corona virus disease (COVID-19) epidemic has a great impact on the real estate industry. The epidemic has caused a downward trend in housing values. It is essential for those who want to buy property during this period of economic recovery to understand the key factors influencing house pricing. In order to identify the factors that influence real estate prices and make house price prediction, this study uses a mathematical model called linear regression to analyze the data. The model is further improved and streamlined by focusing on essential elements. The study successfully pinpoints the most critical factors affecting home prices and makes relatively accurate house price prediction. Notably, the house's number of levels and bedrooms are essential determinants for house price prediction. Understanding these variables enables real estate agencies to concentrate on particular areas for improvement, thereby optimizing the total housing market, which gives buyers a clearer idea of the types of homes they can pick. Real estate brokers can work more productively, increase their competitive edge, and guarantee long-term success in the post-pandemic recovery phase. Furthermore, by building homes with more logical conditions for buyers, brokers may protect their brand identity and nurture client loyalty.

Shanghai House Price Prediction Using Random Forest

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, including nearby amenities. What they know about the house is only its housing characteristics. The objective of the paper is to help these clients put their resources into a bequest properly. The paper collected a wide range of Shanghai real estate data about housing characteristics as characteristics. By harnessing the ensemble learning capabilities of random forests, the paper aims to capture complex relationships and non-linear effects between features, thereby improving prediction accuracy.

An Optimal House Price Prediction Algorithm: XGBoost

An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning (ML) techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare XGBoost, support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction. Our findings present valuable insights and tools for stakeholders, facilitating more accurate property price estimates and, in turn, enabling more informed decision making to meet the housing needs of diverse populations while considering budget constraints.

House Price Prediction Based on Machine Learning

House Price Prediction Based on Machine Learning

The Xgboost-Bagging Approach of Predicting House Prices

The Xgboost-Bagging Approach of Predicting House Prices