House Price Prediction Research Articles

A Study on the Prediction of House Price Index in First‐Tier Cities in China Based on Heterogeneous Integrated Learning Model

To address the difficulty of low prediction accuracy, insufficient model stability, and certain lag associated with a single machine learning model in the prediction of house price, this paper proposes a multimodel fusion house price prediction model based on stacking integrated learning. Firstly, web search data affecting house prices were collected by web crawler technology, and Spearman correlation analysis was performed on the attribute set to reduce its complexity and establish a prediction index system for four first‐tier cities in China. Secondly, with the goal of improving accuracy, diversity, and generalization ability, the types of base learners as well as metalearners are determined, and the parameters of the base learners are optimized using the grey wolf optimization algorithm to produce the GWO‐stacking model, and the experimental results from four datasets demonstrate that the model has high prediction accuracy. Finally, to solve the issue of unintelligible black boxes in machine learning models, we have used the state‐of‐the‐art interpretation method SHAP combined with the LightGBM algorithm to interpret the model, and the result can be used as a basis for real estate policy planning and adjustment and even guide the demand of home buyers, thus improving the efficiency and effectiveness of government policy making.

Analysis and prediction of second-hand house price based on random forest

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 by the real house price is small; it also indicates the accuracy of the RF model and demonstrates its good application value.

Artificial intelligence algorithms to predict Italian real estate market prices

PurposeThe assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.Design/methodology/approachAn extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.FindingsThe models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).Research limitations/implicationsAll the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.Practical implicationsThe accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.Originality/valueTo date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.

Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM).

This paper develops an artificial intelligence based automated valuation model (AI-AVM) using the boosting tree ensemble technique to predict housing prices in Singapore. We use more than 300,000 private and public housing transactions in Singapore for the period from 1995 to 2017 in the training of the AI-AVM models. The boosting model is the best predictive model that produce the most robust and accurate predictions for housing prices compared to the decision tree and multiple regression analysis (MRA) models. The boosting AI-AVM models explain 91.33% and 94.28% of the price variances, and keep the mean absolute percentage errors at 8.55% and 5.34% for the public housing market and the private housing market, respectively. When subject the AI-AVM to the out-of-sample forecasting using the 2018 housing sale samples, the prediction errors remain within a narrow range of between 5% and 9%.

House Price Prediction Using Linear Regression

Index Terms: Regression model, House price prediction, machine learning, housing market, Arima model, artificial neural network, support vector machine, random forest, dataset.

Housing Price Prediction Based on Multiple Linear Regression

In this paper, the author first analyzes the major factors affecting housing prices with Spearman correlation coefficient, selects significant factors influencing general housing prices, and conducts a combined analysis algorithm. Then, the author establishes a multiple linear regression model for housing price prediction and applies the data set of real estate prices in Boston to test the method. Through the data analysis and test in this paper, it can be summarized that the multiple linear regression model can effectively predict and analyze the housing price to some extent, while the algorithm can still be improved through more advanced machine learning methods.

Boosting house price predictions using geo-spatial network embedding

Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed for real estate price prediction. Most of the existing techniques rely on different house features to build a variety of prediction models to predict house prices. Perceiving the effect of spatial dependence on house prices, some later works focused on introducing spatial regression models for improving prediction performance. However, they fail to take into account the geo-spatial context of the neighborhood amenities such as how close a house is to a train station, or a highly-ranked school, or a shopping center. Such contextual information may play a vital role in users’ interests in a house and thereby has a direct influence on its price. In this paper, we propose to leverage the concept of graph neural networks to capture the geo-spatial context of the neighborhood of a house. In particular, we present a novel method, the geo-spatial network embedding (GSNE), that learns the embeddings of houses and various types of points of interest (POIs) in the form of multipartite networks, where the houses and the POIs are represented as attributed nodes and the relationships between them as edges. Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model. Relevant source code for GSNE is available at: https://github.com/sarathismg/gsne .

Support Vector Regression with Gaussian kernel for Housing Prices Prediction

The housing sector is one of the main sources of economic growth in both developing and developed countries. It is reported that nearly half of people buy or sell houses at an inappropriate price. Based on the public data set of Boston housing prices, this essay analyzed the factors affecting house prices and selected the five most important factors based on the decision tree with the ID3 algorithm. Then, this essay developed the support vector regression (SVR) with Gaussian kernel to predict housing prices. Experimental results showed that our method achieves superior accuracy and effectiveness compared with the SVR with linear kernel, KNN, and decision tree. To verify the applicability of our model, this research applied this model in Beijing housing price data, and it also achieved satisfactory fitting results.

Market news and credibility cues improve house price predictions: An experiment on bounded rationality in real estate

We experimentally study bounded rationality in real estate by observing the effects of market news and media credibility cues on house price predictions. Non-professional market actors read news articles about recent house price trends from online news sources varying in perceived trustworthiness and brand anonymity, and then predicted selling prices of apartments for monetary reward. Boundedly rational actors are expected to make (imperfect) use of public information and rely on contextual ques to infer information quality. Price predictions were in the opposite direction of market trends in a control condition without market news. As hypothesized, (1) market news on price trends increased the accuracy of predictions, and (2) information from a more credible medium had stronger effect on price predictions when its brand was made visible. Systematic inaccuracies in price predictions, their improvement with market information, and reliance on credibility cues suggest that non-professional property valuations are boundedly rational.

House Price Prediction using Machine Learning

Machine Learning is seeing like its growth is much more rapid in this decade. Lots of applications of Machine Learning and algorithms of Machine Learning is evolving day to day. One of the application found in journals which is nothing but house price prediction. Prices of the houses are increasing each and every year which has postulate the modeling of house price prediction. All these models constructed using Machine Learning, help the customers for purchasing a house which is suitable as per their needs. The work which is proposed makes the use of all the attributes or all features of houses such as travelling facility from the location, In the proposed work, the house availability based on desired features of the house and house price prediction are modelled.

Prediction of China’s Housing Price Based on a Novel Grey Seasonal Model

The fluctuation of real estate prices has an important impact on China's economic development. Accurate prediction of real estate market price changes has become the focus of scholars. The existing prediction methods not only have great limitations on the input variables but also have many deficiencies in the nonlinear prediction. In the process of real estate market price forecasting, the priority of data and the seasonal fluctuation of housing price are important influencing factors, which are not taken into account in the traditional model. In order to overcome these problems, a novel grey seasonal model is proposed to predict housing prices in China. The main method is to introduce seasonal factor decomposition into the new information priority grey prediction model. Two practical examples are used to test the performance of the new information priority grey seasonal model. The results show that compared with the existing prediction models, this method has better applicability and provides more accurate prediction results. Therefore, the proposed model can be a simple and effective tool for housing price prediction. At the same time, according to the prediction results, this paper analyzes the causes of housing price changes and puts forward targeted suggestions.

House Price Prediction using Machine Learning Techniques

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 regression, robust regression, lasso regression, elastic net regression, stochastic gradient descent, svm regression, artificial neural network. On a data set, house price prediction has been done by combining all of the above-mentioned strategies to determine which is the most effective. The purpose of the paper is to assist the seller in accurately estimating the selling price of a house. Physical circumstances, and location, among other things, were all taken into account while determining the cost.

House price prediction: A data-centric aspect approach on performance of combined principal component analysis with deep neural network model

House price prediction: A data-centric aspect approach on performance of combined principal component analysis with deep neural network model

House Price Prediction using Machine learning and Image Processing

We demonstrate that these urban features can be recorded by street views and satellite image data and enhance the estimate of house prices. In order to estimate house prices in London, UK, we recommend a pipeline that uses a deep neural network model to automatically extract visual features from images. In calculating the house price model, we use typical housing characteristics, such as age, size, and accessibility, as well as visual features from Google Street View images and Bing aerial pictures. We see promising outcomes where learning to describe a neighborhood’s urban efficiency facilitates the estimation of house prices, even when generalizing to previously unseen London boroughs. We discuss the use of non-linear vs. linear approaches to combine these signals with traditional house pricing models and explain how the interpretability of linear models helps one to specifically derive the visual desirability of neighborhoods as proxy variables that are both of importance in their own right and can be used as inputs to other econometric methods. This is particularly useful as it can be extended elsewhere after the network has been trained with the training data, enabling us to produce vivid complex maps of the desirability of London streets.

Review on House Price Prediction using Machine Learning

Review on House Price Prediction using Machine Learning

Predicting owner-occupied housing values using machine learning: an empirical investigation of California census tracts data

ABSTRACT In this paper, we introduce machine-learning (ML) methods to evaluate one of the key concepts of real estate analysis – the prediction of housing prices in the presence of a large number of covariates. We use several supervised ML tools that are based on regularisation methods – notably Ridge, LASSO, and Elastic Net regressions – and discuss their relative performance in comparison to conventional OLS-based methods. Our empirical results show that the supervised ML methods provide a comprehensive description of the determinants of owner-occupied housing values in the census tracts of California. We find that, compared to the familiar worlds of OLS and WLS, the Ridge, LASSO, and Elastic Net regressions provide relatively better out-of-sample predictions. Among the benefits of shrinkage-based ML methods are their ability to resolve such issues as variable selection and overfitting.

Metrics for evaluating the performance of machine learning based automated valuation models

ABSTRACT Automated Valuation Models (AVMs) based on Machine Learning (ML) algorithms are widely used for predicting house prices. While there is consensus in the literature that cross-validation (CV) should be used for model selection in this context, the interdisciplinary nature of the subject has made it hard to reach consensus over which metrics to use at each stage of the CV exercise. We collect 48 metrics (from the AVM literature and elsewhere) and classify them into seven groups according to their structure. Each of these groups focuses on a particular aspect of the error distribution. Depending on the type of data and the purpose of the AVM, the needs of users may be met by some classes, but not by others. In addition, we show in an empirical application how the choice of metric can influence the choice of model, by applying each metric to evaluate five commonly used AVM models. Finally – since it is not always practicable to produce 48 different performance metrics – we provide a short list of 7 metrics that are well suited to evaluate AVMs. These metrics satisfy a symmetry condition that we find is important for AVM performance, and can provide a good overall model performance ranking.

Prediksi Harga Rumah Menggunakan Web Scrapping dan Machine Learning Dengan Algoritma Linear Regression

Tempat tinggal atau rumah merupakan salah satu dari banyaknya kebutuhan primer bagi manusia. Maka dari itu sangat penting untuk membuat perencanaan agar tiap keluarga dapat memiliki tempat tinggal pribadi. Dalam perencanaan tersebut dibutuhkan prediksi atau perkiraan harga di masa mendatang. Maka, tujuan penelitian ini bertujuan untuk membuat model prediksi harga rumah menggunakan metode machine learning adapun algoritmanya adalah linear regression. Dengan melakukan web scraping untuk mengumpulkan data, melalui beberapa website yang bergelut dibidang jual beli rumah. Adapun menurut developer rumah yang berhasil ditanyakan dilapangan terkait variabel yang mempengaruhi harga rumah diantaranya luas lahan, luas bangunan berdiri, banyaknya kamar tidur, banyaknya kamar mandi, dan ketersediaan tempat parkir mobil. Untuk mendapatkan nilai prediksi yang tinggi penelitian dilakukan secara berulang kali namun nilai prediksi terbesar dengan menggunakan 80% dataset untuk training dan 20% dataset digunakan untuk testing menghasilkan nilai output dengan tingkat akurasi dalam memprediksi sebesar 88%.

House price prediction using polynomial regression with Particle Swarm Optimization

This paper combines the characteristics of the real Washington DC house price estate market and the theoretical model of housing prices to conduct an empirical analysis and comparison of the current mainstream housing price index prediction models. It is found that the current mainstream model only studies the trend of the housing price index itself, and is not sensitive to the characteristics of the house itself. Therefore, this paper uses a multiple regression model to integrate the advantages of external factors and use the improved housing price composition establishing a multiple regression prediction model with the particle swarm optimization. It not only makes up for the disadvantages of poorly determined housing price regression indicators and lack of statistical data in multiple regression prediction, but also enables the model to reflect the inflection point of housing prices in advance. PSO is used for selection of affect variables and regression analysis is used to determine the optimal coefficient in prediction.

Investigation on House Price Prediction with Various Gradient Descent Methods

As a branch of artificial intelligence, machine learning has received a lot of attention and has become a popular research field. It connects the huge database obtained from daily life with data prediction, which is of great help in decision making. In this project, we apply linear regression models and use different gradient descent methods, including general, mini-batch, and stochastic gradient descent, to optimize the error. We used convergence rate and accuracy as evaluation criteria, which will be explained via run time of convergence and mean square error respectively. For the regression models, both single-feature and multi-feature hypothesis models are investigated. In consideration of the risks of multicollinearity and overfitting, only simple linear model and its exponential transformation are adopted. The result indicates that mini-batch gradient descent has the biggest comprehensive advantage.