House Price Prediction Research Articles

Application and Performance Comparison of Compound Neural Network Model based on CNN Feature Extraction in House Price Forecast

Abstract. This study used a total of eight machine learning algorithms to forecast property prices, it not only provides a robust comparison of the predictive power of different algorithms but also significantly advances our understanding of the factors that influence property prices. In this paper, four traditional machine learning algorithms and four neural network models are selected for comparative study and analysis, of which the neural network models include fully connected neural networks (FCNN), convolutional fully connected neural networks (FCNN+CNN), generative adversarial fully connected networks (FCNN+GANs) and generative adversarial convolutional fully connected neural networks (FCNN+GANs+CNN). This study applied to a Kaggle's sample. The results reveal that the models based on FCNN+CNN and FCNN+GANs+CNN perform relatively well in house price prediction, with both obtaining an explanatory power of R as high as 0. 96 and 0. 97, respectively and significantly outperforming traditional machine learning algorithms. It is worth mentioning that the FCNN+CNN model is slightly stronger in terms of error minimization, but both perform better in terms of stability and generalization capabilities. The conclusion is that neural network models generally have better results than traditional algorithms in house price prediction, and the neural network model of CNN composite has significantly better prediction performance.

Comparison of Multiple Linear Regression and Random Forest Regression Models for House Price Prediction in Semarang City Using the CRISP-DM Method

The population density in Semarang City is increasing every year. This requires more potential land to build houses to accommodate the denser population. There are various kinds of house prices based on specifications in Semarang City. This requires the right prediction to get the desired house. This study implements and compares the performance of Multiple Linear Regression (MLR) and Random Forest Regression (RFR) models to predict house prices in Semarang City. The method used in this research is CRISP-DM (Cross-Industry Standard Process for Data Mining) as a data mining process. The data used in this research amounted to 9533 data with 8 variables obtained by web scraping. The data will go through a data preprocessing process then training the model. Next is the evaluation stage, which is carried out to measure the performance of the two models using evaluation metrics, namely R-Squared (prediction accuracy), MSE (Mean Squared Error), and RMSE (Root Mean Squared Error). The results of this study show that the MLR model obtained a prediction accuracy 61.1% with a training and testing data division ratio of 75%: 25%. While the RFR model produces a prediction accuracy 78.4% with a training and testing data division ratio of 90%: 10%. This shows that the RFR model is the best performing model. This research successfully applied the RFR model to the streamlit web framework. The final result of this research is a website that can be used by the public to predict house prices according to criteria in Semarang City.

House Price Pridiction Using Machine Learning

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 proposed model considers as the most accurate model used for calculating the house price and provides a most accurate prediction. This provides a brief introduction which will be needed to predict the house price. 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. Predicting the price of a house helps for determine the selling price of the house in a particular region and it help people to find the correct time to buy a home. In this task on House Price Prediction using machine learning, our task is to use data to create a machine learning model to predict house prices in the given region. 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 improve the efficiency of the model. 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

Factors influencing housing prices: A comparative study using multiple linear regression and random forest

Abstract. The method is to construct a multiple linear regression model to examine the variables that significantly affect US home prices. The dataset has 13 variables and 545 observations, and it was obtained via Kaggle. This paper also considers comparing the multiple linear regression models and random forest model for predicting house prices and thus concludes which model can most accurately predict house prices. The result indicates that the factors including area, quantity of bedrooms and bathrooms, the existence of a basement, and state of the furnishings, etc. significantly impact housing prices. By comparing Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), the conclusion suggests that the multiple linear regression model marginally surpasses the random forest regarding the accuracy of the predictions, despite the fact that both models function similarly. This research offers practical applications for enhancing real estate market strategies and provides valuable insights into the factors influencing housing prices.

Predicting Housing Prices Using Supervised Machine Learning Models

Abstract: Variations in housing prices exert a profound impact on economic policies and personal financial decisions. This study aims to delve into the factors that affect housing prices and construct a predictive model using machine learning techniques. Machine learning enables computers to acquire knowledge from data and make predictions or decisions. This study forcasts house prices by analyzing the characteristics of data from Seattle, Washington using machine learning multiple linear regression, polynomial regression, and K-nearest neighbors regression (KNN). The findings of this investigation demonstrate that the polynomial regression model is the most accurate in predicting housing prices and can capture nonlinear relationships in the data more effectively than multiple linear regression and K-nearest neighbors regression. The main factors affecting housing prices are interior living space and the construction and design of buildings. These insights hold potential for enhancing government policies, facilitating effective land use decision-making by planners, and enabling investors to make more informed choices.

Predicting House Prices Using Regression Model

This paper shows a study on the development of a predictive model for house prices. The model uses machine learning techniques to analyze a large dataset of of housing market data, including demographic information, real estate trends, and economic indicators. The goal of the model is to accurately predict the sale prices of a house given its features and traits. To achieve this target, the study employs feature selection and engineering methods to determine the most significant predictors of house prices [1]. The results of the model are evaluated using standard metrics, sucha as root mean squared error [RMSE] and mean absolute error [MAE]. The results show that the proposed model outperforms benchmark model and provides a reliable prediction of house prices. The model can be used by real estate professionals, policymakers, and homebuyers to gain insights into the housing market and make the right choice [3]. Keywords- Regression, prediction, house price, Machine Learning.

Housing Price Prediction - Machine Learning and Geostatistical Methods

Abstract Machine learning algorithms are increasingly often used to predict real estate prices because they generate more accurate results than conventional statistical or geostatistical methods. This study proposes a methodology for incorporating information about the spatial distribution of residuals, estimated by kriging, into selected machine learning algorithms. The analysis was based on apartment prices quoted in the Polish capital of Warsaw. The study demonstrated that machine learning combined with geostatistical methods significantly improves the accuracy of housing price predictions. Local factors that influence housing prices can be directly incorporated into the model with the use of dedicated maps.

Research on house price prediction in UK based on linear regression model

This paper aims to analyze the factors that affect housing prices and predict the trend of housing prices through a linear regression model. This article will use a linear regression model to analyze data on UK house prices from 1952 to 2022. And selected 12 different factors that affect housing prices, using a linear regression model to predict the fluctuations of housing prices. At the same time, a multiple prior regression model was used to explore the importance of each factor in affecting house prices. And the significance of these variables was also compared. The results show that the location of the house, per capita income, housing environment, immigration rate, actual deposit rate and actual loan rate have a significant linear relationship with house price changes, while per capita productivity and house type after the epidemic do not have a significant linear relationship with house price changes. Therefore, UK house prices can be predicted based on the above factors.

Boosting house price estimations with Multi-Head Gated Attention

Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relationships that affect property values. To address these challenges, we have developed a new method called Multi-Head Gated Attention for spatial interpolation. Our approach builds upon attention-based interpolation models and incorporates multiple attention heads and gating mechanisms to better capture spatial dependencies and contextual information. Importantly, our model produces embeddings that reduce the dimensionality of the data, enabling simpler models like linear regression to outperform complex ensembling models. We conducted extensive experiments to compare our model with baseline methods and the original attention based interpolation model. The results show a significant improvement in the accuracy of house price predictions, validating the effectiveness of our approach. This research advances the field of spatial interpolation and provides a robust tool for more precise house price evaluation. Our GitHub repository.11Final_file/tree/main/ASI-main. contains the data and code for all datasets, which are available for researchers and practitioners interested in replicating or building upon our work.

House Price Prediction Taking Account of the Influence of COVID-19

Machine learning algorithms are utilized in various practical applications and academic research. Due to the advent of digital technology, there is now a greater availability of massive, organized, and georeferenced datasets. This has simplified the use of algorithms for trend analysis and detection as well as forecasting, which helps users make decisions. The purpose of this study is to determine which Machine Learning (ML) algorithms are most effective in projecting housing price and to assess how the COVID-19 has affected these prices. The method includes the stages of data preprocessing, feature manipulation, hyperparameter tuning, evaluation, selection, and, in the end, interpretation of the model. Another objective is to assess and compare several ensemble learning strategies, comprising bagging techniques like random forest, and boosting techniques like Gradient Boosting Regressor, using a linear regression model. The results of this study confirm that the real estate sector is very resistant to pandemics, as the decline in prices was not as severe as initially anticipated. Among the several models that were examined, extreme gradient boosting demonstrated the highest level of accuracy, albeit with a minimal difference.

Multiple Machine Learning Models in House Price Prediction: Performance Evaluation and Comparison

House pricing is one of the most critical concerns for people. Accurately predicting house prices is essential for participants and investors in the real estate market. This study delves into the realm of predictive modeling for house prices using a multimodal approach in machine learning. The research methodology spans various stages including data collection, preprocessing, feature engineering, and model selection. Based on a dataset sourced from Kaggle, housing data from Iowa comprising 79 pertinent features influencing property prices is utilized. Performance evaluation is conducted using the root mean square error (RMSE), comparing the efficacy of linear regression models such as Lasso regression, Kernel Ridge regression, and ElasticNet regression against tree-based models including XGBoost, Gradient Boosting, and LightGBM. Additionally, the study implements two stacked ensemble models to identify the most optimal predictive model for house prices. Experimental procedures involve training and evaluating these models on specific datasets to gauge their predictive accuracy. Findings and discussions underscore the comparative analysis of diverse machine learning models within the context of house price prediction. Moreover, the study employs the Gradient Boosting method in machine learning to pinpoint the ten most influential features impacting house prices. Recommendations for future research are provided to enhance the precision and robustness of house price prediction models. This research contributes to the advancement of predictive modeling in the real estate domain, offering insights into the efficacy of various machine learning techniques for forecasting property prices.

House Price Prediction Using Machine Learning and Artificial Intelligence

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.

Research on Housing Price Prediction Based on Machine Learning

Research on Housing Price Prediction Based on Machine Learning

Modeling of the effect of transportation system accessibility on residential real estate prices: The case of Washington metropolitan area, USA

Deep learning accurate predictions of house prices are essential for prospective homeowners, investors, appraisers, and insurers. However, some studies lack accuracy as they overlook critical factors like accessibility and economic attributes that influence house prices. This paper aims to predict house prices by considering structural, locational, accessibility, and economic attributes, while also exploring the effect of accessibility on housing prices. The dataset contains 2,019,663 real estate transaction records from 1975 to 2018 in the Washington metropolitan area, obtained from the Zillow website. In this study, the accessibility index is calculated using Distance, Cumulative Opportunities, and Gravity measures, with the gravity measure surpassing others due to its consideration of both land use and transportation aspects. Economic attributes are then utilized to predict the average monthly house price using deep learning algorithms such as LSTM, GRU, and Simple RNN, with the Simple RNN demonstrating superior performance. Following the amalgamation of structural and locational attributes with the accessibility index and average house prices, various machine learning algorithms—including Linear Regression, Lasso, Ridge, Random Forest, GBM, LightGBM, XGBoost, Decision Tree, AdaBoost, Artificial Neural Network, and Stacked Generalization—are employed for prediction. Subsequent evaluation reveals that Stacked Generalization (ANN + LightGBM) provides the best performance, with an R-squared value of 0.96 and RMSE of $23,290. Moreover, this paper identifies accessibility index thresholds (80,003 for large buildings and 160,103 for small buildings) and demonstrates that a higher accessibility index leads to lower housing prices, attributed to noise pollution, decreased privacy, and increased supply responses.

A Comparative Study of Regression Models for Housing Price Prediction

A Comparative Study of Regression Models for Housing Price Prediction

Prediction of new housing prices in Changsha urban area based on multiple machine learning algorithms: A comparative analysis

<p>As China’s pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are used to construct forecasting models, and the best-performing model is selected by conducting a comparative analysis of the forecasting errors obtained between these models. The research found that machine learning is a practical approach to property price prediction, with least squares regression and Lasso regression providing relatively more convincing results.</p>

Machine learning for real estate valuation: Astana, Kazakhstan case

Purpose of this research is to investigate the accuracy of machine learning models in forecasting and evaluating house prices, and to understand the key factors that impact pricing. The study involved analyzing data scraped from real estate ads in the “sale of secondary housing” category on the website krisha.kz. The paper emphasizes the importance of understanding the factors that affect house prices, such as quality, location, size, and building materials. It was concluded that these factors have a strong correlation with house price prediction. The information available on krisha.kz was found to be a useful resource for finding good apartments. The data collected by the scraper was analyzed by models: Linear regression (LR), interactions linear regression (ILR), robust linear regression (RLR), fine tree regression (FTR), medium tree regression (MTR), coarse tree regression (CTR), linear support vector machine (LSVM), quadratic SVM (QSVM), medium gaussian SVM (MGSVM), rational quadratic gaussian process regression (RQGPR), boosted trees (BoosT), bagged trees (BagT), neural network based on the bayesian regularization algorithm (BR-BPNN). BR-BPNN showed better results than other models, with an MSE of 32.14 and R of 0.9899.

Application of the ARIMA model in house price index in Malaysia

The factors that affecting the escalating price of houses in Malaysia are driven by factors such as population growth, income dynamics, interest rates, and GDP. This phenomenon has notably outpaced the growth of household incomes, thus majorly impacting Malaysians. The study’s primary goal is to forecast the housing price index in Malaysia from the best model obtained using Box-Jenkins method. aligning with the 2018-2025 National Housing Policy objectives, utilizing advanced machine learning and time series modeling. The objectives guide the research: to find the best model for predicting house price index in Malaysia using Box-Jenkins Method. Utilizing secondary data from the National Property Information Centre (NAPIC) spanning from 1988 to 2023, the study employed the analytical method of ARIMA. The results favored the ARIMA (1,1,1) model as the best model in predicting housing price indexes. This offers an excellent forecasting model for residential properties towards gaining better understanding of their pricing dynamics and offers potential solutions to the issue of housing affordability for Malaysians.

Modeling Nairobi Residential Real Estate Prices using ARIMA

The residential real estate market is big and affords investors investment opportunities. The price changes are key in determining the overall return. Structural and atheoretical models are the two main approaches to modeling real estate prices. Structural models link prices to fundamental factors such as economic indicators and property supply, amongst others. Atheoretical models attempt to predict prices by leveraging on the statistical properties of time series data and may be extended to augment fundamental factors. This study focused on time series modeling using ARIMA. The objective of the paper was to identify a suitable ARIMA model that is efficient in predicting house prices in Nairobi. The training data was for the period 2010Q3 to 2019Q2. The out of sample test data was for six quarters: 2019Q3 to 2020Q4. The Box-Jenkins methodology was adopted. Seven ARIMA models and six AR models were identified, estimated, and used in predicting prices using out of sample data. The study found out that AR models outperformed ARIMA models. The paper contributes to knowledge being among the first to apply ARIMA in Nairobi house market using hedonic house prices. The paper may inform investment strategy and portfolio management by investors. It may inform policy since house price forecasts may have social and economic effects.

House price prediction

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 forest model to predict house price. However, the author think neural network model can also have good performance in house prices. So, this paper is about building a better neural network model to predict house prices. This paper describes how to process data before training two models and compare their prediction accuracy. After that, this paper describes new neural network and train this model, which, after training, will output its accuracy. Luckily, after a long-time training, the author gets a neural network model to predict house price, it has a higher accuracy in predicting house price than two tradition models that we used.