Relevant bibliographies by topics / House Price Prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'House Price Prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 June 2025

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Journal articles on the topic "House Price Prediction"

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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, and some other factors which could affect house demand and supply. A practical and composite data pre-processing, creative feature engineering method is investigated with limited dataset and data features. This model is used to predict the house prices so as to cut down the complications faced by the customers. The present method where customers reach real-estate agents and search for houses in their budget, and should analyze whether a particular price is accurate or not. To overcome this our proposal is used. This system makes optimal use of the Machine Learning Algorithms. By extracting data from datasets of different houses, preprocessing the data and model is built using that data using Regression. The algorithm used for the model building is KNN (K Nearest Neighbor) Algorithm. This system design is modularized into various categories.
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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 front end of the system. At last, House Price Prediction Website will be very helpful in detecting the prices of the houses and keeping the record of the high and low of the prices. 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.
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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 is presented
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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. Additionally, research shows that advanced techniques such as geographic analysis and economic analysis are used to improve forecast accuracy. The findings underscore the importance of using accurate statistics and analytical methods to predict house prices, providing valuable information to stakeholders in real estate investment, urban planning and policy making. This retrospective focuses on summarizing the methodology, key findings and conclusions of research in the field of house price forecasting. Adjustments may be made based on the specific results and methods used in a particular study Keyword: House Price Prediction, Machine Learning.
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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 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.
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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 of different ML techniques in predicting housing prices Our goal is to enhance predictability of models through rigorous analysis, thus facilitating informed decision-making when it comes to housing transactions, investments, and policy implementations through our research.
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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, and Catboost. After cross-validation tests, the CatBoost, algorithm with the lowest Mean Square Error (MSE) is regarded as the most accurate algorithm to predict house prices. The analytic results show that the house price is dominated by the location features such as area and block.
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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 analysis based on linear regression, ridge regression, Lasso regression, and Elastic Net regression, with the aid of machine learning with feature selection. The proposed prediction model for house prices was evaluated on a machine learning housing dataset, which covers 1,460 records and 81 features. By comparing the predicted and actual prices, it was learned that our model outputted an acceptable, expected values compared to the actual values. The error margin to actual values was very small. The comparison shows that our model is satisfactory in predicting house prices.
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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 house buying and selling platform does not provide a house price prediction feature that is in accordance with user specifications. This means someone who is planning to buy a house does not get an initial idea of the costs that must be spent to own the desired home. Therefore, in this study, researchers will design a web app-based house price prediction system that can make it easier for users to get predictions of the desired house price. In this study the prediction algorithms to be used are linear regression and random forest. Both algorithms will be analyzed for their performance and then the algorithm with the best level of accuracy will be applied as a predictive model which will be integrated with the user interface display.
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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, buyers, real estate agents, and policymakers by enabling data-driven decisions. This paper discusses the dataset, preprocessing techniques, feature selection, model implementation, evaluation metrics, and challenges, offering insights into how predictive modeling can enhance transparency and efficiency in the housing market.
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Dissertations / Theses on the topic "House Price Prediction"

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Aghi, Nawar, and Ahmad Abdulal. "House Price Prediction." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-20945.

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This study proposes a performance comparison between machine learning regression algorithms and Artificial Neural Network (ANN). The regression algorithms used in this study are Multiple linear, Least Absolute Selection Operator (Lasso), Ridge, Random Forest. Moreover, this study attempts to analyse the correlation between variables to determine the most important factors that affect house prices in Malmö, Sweden. There are two datasets used in this study which called public and local. They contain house prices from Ames, Iowa, United States and Malmö, Sweden, respectively.The accuracy of the prediction is evaluated by checking the root square and root mean square error scores of the training model. The test is performed after applying the required pre-processing methods and splitting the data into two parts. However, one part will be used in the training and the other in the test phase. We have also presented a binning strategy that improved the accuracy of the models.This thesis attempts to show that Lasso gives the best score among other algorithms when using the public dataset in training. The correlation graphs show the variables' level of dependency. In addition, the empirical results show that crime, deposit, lending, and repo rates influence the house prices negatively. Where inflation, year, and unemployment rate impact the house prices positively.
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Engström, Isak, and Alan Ihre. "Predicting house prices with machine learning methods." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260140.

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In this study, the machine learning algorithms k-Nearest-Neighbours regression (k-NN) and Random Forest (RF) regression were used to predict house prices from a set of features in the Ames housing data set. The algorithms were selected from an assessment of previous research and the intent was to compare their relative performance at this task. Software implementations for the experiment were selected from the scikit-learn Python library and executed to calculate the error between the actual and predicted sales price using four different metrics. Hyperparameters for the algorithms used were optimally selected and the cleaned data set was split using five-fold cross-validation to reduce the risk of bias. An optimal subset of hyperparameters for the two algorithms was selected through the grid search algorithm for the best prediction. The Random Forest was found to consistently perform better than the kNN algorithm in terms of smaller errors and be better suited as a prediction model for the house price problem. With a mean absolute error of about 9 % from the mean price in the best case, the practical usefulness of the prediction is rather limited to making basic valuations.<br>I den här studien användes maskininlärningsalgoritmerna k-Nearest-Neighbours regression och Random Forest regression för att förutsäga huspriserna från en uppsättning variabler i Ames Housing datasetet. Algoritmerna valdes utifrån en bedömning av tidigare forskning och avsikten var att jämföra deras relativa prestanda i lösandet av denna uppgift. För experimentet valdes programvaruimplementeringar från Pythonbiblioteket scikit-learn och kördes för att beräkna felet mellan det faktiska och förutsedda försäljningspriset med fyra olika mätsätt. Hyperparametrar för de använda algoritmerna valdes optimalt och den rengjorda datamängden delades med femfaldig korsvalidering för att minska risken för partiskhet med hänsyn till datat. En optimal delmängd av hyperparametrar valdes även ut med algoritmen grid search för bästa möjliga förutsägelse. Random Forest-algoritmen visade sig konsekvent prestera bättre än k-NN-algoritmen i bemärkelsen minimalt fel och är en mer lämplig modell för problemet. Med ett genomsnittligt absolutfel på ca 9 % från det genomsnittliga priset i bästafallet är den praktiska användbarheten av förutsägelsen tämligen begränsad till att göra grundläggande värderingar.
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Sollander, Robin. "Prediktion av huspriser i Falun / Prediction of House Prices in Falun." Thesis, KTH, Matematik (Avd.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105808.

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I denna uppsats tillämpas multipel regressionsanalys med syfte att predikera huspriser i Falun. Data som består av dels priset vid ett antal husförsäljningar och dels ett antal eventuellt samvarierande förklarande variabler analyseras. Två lämpliga, modeller som på ett så precist och enkelt sätt som möjligt förutsäger en kommande försäljning av ett hus tas fram. I den första finns en mäklarfirmas utropspris med som förklarande variabel i den andra inte. Prediktionsförmågan för de båda modellerna blir inte användbar i praktiken men bättre då utropspris finns med. De förklarande variablerna blir för modellen med utropspris också boarea, om huset har garage och om huset ligger i stadsdelen Slätta. För modellen utan utropspris är de förklarande variablerna taxerat markvärde, taxerat byggnadsvärde och tomtarea.<br>This paper applies multiple regression analysis to predict house prices in Falun. Data consisting of sales price and a number of possible explanatory variables is analyzed. Two appropriate, models that are as precise in predicting and simple as possible are developed. In the first model a home sales office’s starting price is included as an explanatory variable in the other not. The predictive ability of the two models is not useful in practice, but better when starting price is included. The explanatory variables are for the model with the starting price included also living area, if the house has a garage or not and if the house is located in the district Slätta. For model without starting price the explanatory variables are taxed land value, taxed building value and land area. 3
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Oxenstierna, Johan. "Predicting house prices using Ensemble Learning with Cluster Aggregations." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-345157.

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The purpose of this investigation, as prescribed by Valueguard AB, was to evaluate the utility of Machine Learning (ML) models to estimate prices on samples of their housing dataset. Specifically,the aim was to minimize the Median Absolute Percent Error (MDAPE) of the predictions. Valueguard were particularly interested in models where the dataset is clustered by coordinates and/or attributes in various ways to see if this can improve results. Ensemble Learning models with cluster aggregations were built and compared against similar model counterparts which do not partition the data. The weak learners were either lazy kNN learners (k nearest neighbors), or eager ANN learners (artificial neural networks) and the test set objects were either classified to single weak learners or tuned to multiple weak learners. The best results were achieved by the cluster aggregation model where test objects were tuned to multiple weak learners and it also showed the most potential for improvement.
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Revend, War. "Predicting House Prices on the Countryside using Boosted Decision Trees." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279849.

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This thesis intends to evaluate the feasibility of supervised learning models for predicting house prices on the countryside of South Sweden. It is essential for mortgage lenders to have accurate housing valuation algorithms and the current model offered by Booli is not accurate enough when evaluating residence prices on the countryside. Different types of boosted decision trees were implemented to address this issue and their performances were compared to traditional machine learning methods. These different types of supervised learning models were implemented in order to find the best model with regards to relevant evaluation metrics such as root-mean-squared error (RMSE) and mean absolute percentage error (MAPE). The implemented models were ridge regression, lasso regression, random forest, AdaBoost, gradient boosting, CatBoost, XGBoost, and LightGBM. All these models were benchmarked against Booli's current housing valuation algorithms which are based on a k-NN model. The results from this thesis indicated that the LightGBM model is the optimal one as it had the best overall performance with respect to the chosen evaluation metrics. When comparing the LightGBM model to the benchmark, the performance was overall better, the LightGBM model had an RMSE score of 0.330 compared to 0.358 for the Booli model, indicating that there is a potential of using boosted decision trees to improve the predictive accuracy of residence prices on the countryside.<br>Denna uppsats ämnar utvärdera genomförbarheten hos olika övervakade inlärningsmodeller för att förutse huspriser på landsbygden i Södra Sverige. Det är viktigt för bostadslånsgivare att ha noggranna algoritmer när de värderar bostäder, den nuvarande modellen som Booli erbjuder har dålig precision när det gäller värderingar av bostäder på landsbygden. Olika typer av boostade beslutsträd implementerades för att ta itu med denna fråga och deras prestanda jämfördes med traditionella maskininlärningsmetoder. Dessa olika typer av övervakad inlärningsmodeller implementerades för att hitta den bästa modellen med avseende på relevanta prestationsmått som t.ex. root-mean-squared error (RMSE) och mean absolute percentage error (MAPE). De övervakade inlärningsmodellerna var ridge regression, lasso regression, random forest, AdaBoost, gradient boosting, CatBoost, XGBoost, and LightGBM. Samtliga algoritmers prestanda jämförs med Boolis nuvarande bostadsvärderingsalgoritm, som är baserade på en k-NN modell. Resultatet från denna uppsats visar att LightGBM modellen är den optimala modellen för att värdera husen på landsbygden eftersom den hade den bästa totala prestandan med avseende på de utvalda utvärderingsmetoderna. LightGBM modellen jämfördes med Booli modellen där prestandan av LightGBM modellen var i överlag bättre, där LightGBM modellen hade ett RMSE värde på 0.330 jämfört med Booli modellen som hade ett RMSE värde på 0.358. Vilket indikerar att det finns en potential att använda boostade beslutsträd för att förbättra noggrannheten i förutsägelserna av huspriser på landsbygden.
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LIN, YI-PING, and 林翊平. "Data Mining in House Price Prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5sqy36.

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碩士<br>銘傳大學<br>資訊工程學系碩士班<br>107<br>Real estate refers to buildings (civil law) that are immovable and can be fixed at the address, including the land and the houses on it. For the public, the house or the home is included in the food and clothing industry - live, this focus. Therefore, buying a house is a big problem that almost everyone must face. Whether it is seeking to buy or sell between real estate agents or friends or relatives, it is inevitable that the price will be mentioned in the end, that is, the price of house prices depends on the factors of the house itself. The surrounding environmental factors, regional factors, and even the overall factors, the above total factors may be reflected in the housing prices, how to use data exploration technology to analyze the significance of housing prices and the above factors, and to correlate the relevant factors to estimate the reasonable selling price of houses. This is a topic worth exploring, and finally build a predictive model to predict the prices of future data of house price. The source of this research is based on the actual price of the Ministry of the Interior and the analysis of the information on the website of the Ministry of Housing, and the data is brought into linear regression, random forest, XGboost and compare the correct rate and related analysis, and gradually analyze whether the combination of variables is related. The final model can be used to predict data from different years or different area.
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Lai, Chih-Pin, and 賴智彬. "Prediction of House Price Index in Taipei." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/6w5p3s.

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碩士<br>國立高雄應用科技大學<br>金融資訊研究所<br>102<br>Domestic prices have been high in recent years, April 2013, Mr. Minister of Finance Mr. Zhang Shenghu Taipei Vice Mayor Zhang Jin and held a press conference and hoped to lower 30% of real estate price by tax. It refers that the real estate market is very hot now. Discard of government intervention, the purpose of the study is whether we can use the price index correlation with other macroeconomic variables to predict future price movements and the factors to affect the fluctuation of real estate market in Taiwan, in order to provide reference for the academic, commercial purposes, or to provide the first purchase group, first repurchase group the awareness of price change, and to provide objective indicators for real estate investors and researchers to make responses. In this study, we use 106 data, from January 2005 to November 2013,to predict future house price index in greater Taipei area and analyze the data by a random walk(RW), multiple regression analysis(MR), stepwise regression analysis (SW), autoregressive integrated moving average model (ARIMA model) time series method. And analyze the data by mean squared error (MSE), sum of square for error (SSE) and absolute error (MAE), three kinds of assessment criteria, Performance comparison of different methods to predict the performance. Then compare the results of different methodologies to accurately predict changes in the 2nd hand real estate market in Taiwan. The empirical results show that in terms of predicting the performance, a autoregressive integrated moving average model is the best way to predict the performance, and the next one is random walk,and then is Multiple regression analysis.Stepwise regression analysis is the worst. In terms of prediction accuracy, Multiple regression analysi prediction accuracy was highest, Random Walk prediction accuracy rate ranked the next, Stepwise regression analysis and autoregressive integrated moving average modeand other two prediction accuracy was the worst. In terms of correlation between macroeconomic variables and the price index, Taiwan's five major banks home loan interest rates, Consumer price index, Construction Cost Index, Consumer price index housing rental category, the unemployment rate, TAIEX Index and the monthly average price of international crude oil in Dubai, these seven variables and overall economic relevance price index are more significant and the correlation are higher.
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Po-HungChen and 陳柏宏. "Integrated covariate correlative andgeographically weighted model for house price prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/7q62hf.

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碩士<br>國立成功大學<br>測量及空間資訊學系<br>107<br>House prices are affected by numerous reasons. Government policies, economic growth, and land use may cause regional housing price fluctuations. Thus, the housing price estimation problem will be challenging. This study aims to provide simple and efficient ways to estimate house prices. The normal regression model may not the best choice for estimation due to different house prices and heterogeneities in space. This paper proposed a covariate weighted regression (CWR) house price estimation model, which is an extended geographically weighted regression (GWR), under the conditions of various districts and house types. The GWR and the machine learning models are simultaneously used to verify the model performance and evaluate practical applications of datasets from actual selling prices of real estate and house price information websites. Results show that the proposed model has better performance than machine learning models in most of cases. Compared with the proposed model, in which only house age and building floor area are considered, the RMSE of machine learning and GWR models can be improved by 8.2% and 4.5%, respectively. Therefore, CWR can effectively reduce estimation errors from traditional spatial regression models and provide novel and feasible models for house price estimation.
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Lin, Yu-Chuan, and 林右詮. "A House Price Prediction Integrated Web Service System of Taiwan Districts." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/5uh826.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>104<br>Buying a house for most people is not an easy thing in Taiwan. Therefore, they have to consider many factors. For houses’ appearance factors and for geographical factors, these factors will effect directly or indirectly effect the value of house itself. Before buying house, house buyers maybe use Internet to inquire the information of house for sale. And there are many websites of house for sale on the internet such as Yungching.com, Etwarm.com, Xinyi.com, etc. For house buyers, they always concern about the trend of house price of the house they want. Because of the websites of house for sale only providing the current price of houses and details of it, it doesn’t provide the trend of price for specific house type. Hence, it lacks of the integrated service on internet. Along with the Actual Price Registration Data that Dept. of Land Administration announces becomes more popular and transparent than before, many web-sites of house for sale only take it to do the statistics for the average price of specific house type in current, not for the prediction of the house price. Consequntly, our system propose an integrated web service of house price prediction and current information of houses for sale. They can choose the house type they want by our web service, and utilize our system to do a series of analytical methods and prediction model to do the prediction of the house price. In the experiment result, we use hitrate to validate the legitimacy of our prediction interval we produce. For the six municipalities, there is about half of the hitrate above 75%. It implys that our analysis of prediction method has some effect. At last we will present the webpage of prediction result and show the information of houses for sale that in accordance with the specific house type that house buyers choose. We also choose 30 interviewers to do the System Usability Scale of our system, and the result is between “GOOD” and “EXCELLENT”, it indicates that this system is usable.
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Hsieh, Zih-Chen, and 謝子宸. "House Price Prediction Model with Consideration of Neighborhood Features – A Case Study of Taiwan." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/nm783x.

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碩士<br>國立交通大學<br>網路工程研究所<br>106<br>Nowadays, most research and services of house price prediction in Taiwan focus on house characteristics and seldom take neighborhood/environmental features into consideration. However, attributes that affect house price not only contain house attributes, but also include neighborhood attributes and temporal trend of house price. This thesis used open data of real estate transaction data in Taipei City and New Taipei City, analyzed the effect of neighborhood attributes on house price to define a set of effective neighborhood attributes, and finally proposed a model that combine house attributes, neighborhood attributes, and temporal trend of house price to make house price prediction. In experiments, results indicated that neighborhood-considering prediction model has a better performance in prediction loss. And, in comparison to static model, the time-series model has a better prediction capability. Finally, based on the proposed model, this thesis also proposed a house price prediction web system that for help users to estimate budget when buying houses. Also, the experiment showed that service has same prediction performance and reliability as well as model.
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Books on the topic "House Price Prediction"

1

Deghi, Andrea, Mitsuru Katagiri, Sohaib Shahid, and Nico Valckx. Predicting Downside Risks to House Prices and Macro-Financial Stability. International Monetary Fund, 2020.

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Deghi, Andrea, Mitsuru Katagiri, Sohaib Shahid, and Nico Valckx. Predicting Downside Risks to House Prices and Macro-Financial Stability. International Monetary Fund, 2020.

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Deghi, Andrea, Mitsuru Katagiri, Sohaib Shahid, and Nico Valckx. Predicting Downside Risks to House Prices and Macro-Financial Stability. International Monetary Fund, 2020.

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Book chapters on the topic "House Price Prediction"

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Sobana, P., M. Balakumaran, S. Bharathkumar, P. Boopathi, and J. Harish. "House price prediction using machine learning." In Challenges in Information, Communication and Computing Technology. CRC Press, 2024. http://dx.doi.org/10.1201/9781003559085-121.

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Kamal, Navia, Ekta Chaturvedi, Siddharth Gautam, and Shruti Bhalla. "House Price Prediction Using Machine Learning." In Emerging Technologies in Data Mining and Information Security. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9774-9_73.

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Rao, A. K., Amit Kumar, and Amit Srivastava. "House price prediction using supervised learning." In Advances in Electronics, Computer, Physical and Chemical Sciences. CRC Press, 2025. https://doi.org/10.1201/9781003616252-69.

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Vineeth, Naalla, Maturi Ayyappa, and B. Bharathi. "House Price Prediction Using Machine Learning Algorithms." In Soft Computing Systems. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1936-5_45.

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Vasani, Hemin, Harshil Gandhi, Shrey Panchal, and Shakti Mishra. "House Price Prediction Using Advanced Regression Techniques." In Emerging Research in Computing, Information, Communication and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5482-5_32.

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Trehan, Praharsh, Subham Nayak, and G. Ramya. "House Price Prediction Using Machine Learning Algorithm." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3191-6_24.

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Zhou, Jincheng, Tao Hai, Ezinne C. Maxwell-Chigozie, et al. "Effective House Price Prediction Using Machine Learning." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37164-6_32.

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Zhou, Yiqian. "Stacking-Based Model for House Price Prediction." In Applied Economics and Policy Studies. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0523-8_88.

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Akour, Iman, Mohammed T. Nuseir, Muhammad Turki Alshurideh, Haitham M. Alzoubi, Barween Al Kurdi, and Ahmad Qasim Mohammad AlHamad. "Machine Learning Empowered House Price Prediction Model." In Studies in Big Data. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-31801-6_19.

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Xiao, Yuling. "Probability Sparse Attention Based House Price Prediction." In Advances in Economics, Business and Management Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-676-5_49.

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Conference papers on the topic "House Price Prediction"

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Kumar, Anurag, Kalyani Singh, Aman Raj, and Aryan Shakya. "House Price Prediction Using ML." In 2024 International Conference on Sustainable Power & Energy (ICSPE). IEEE, 2024. https://doi.org/10.1109/icspe62629.2024.10924379.

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Rathore, Saurabh Pratap Singh, Mohammed Akram Khan, Saurabh Kumar, Arvind Hans, Pallavi Gangwar, and Chinmay Jain. "House Price Prediction Using Machine Learning." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911214.

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Sharma, Madhuri, Seema Bushra, Abdul Wadood Siddiqui, Puja Kumari, R.Dhanusha, and Deepak Jain. "House Price Prediction Using Machine Learning." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911228.

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Mishra, Amandeep, Jason, Yogesh Singh, P. Rajakumar, N. Partheeban, and Indervati. "House Price Prediction Using Machine Learning." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895110.

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Reshma, Tushar Semwal, Shashank Pareek, and Shruti Kuhar. "House Price Prediction using Machine Learning Approaches." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10687534.

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Verma, Abhay, Durgesh Singh, Netra Patil, Sagar G. Mohite, Sakshi Ranjan, and Mohit Raj. "Enhanced House Price Prediction Using Machine Learning Techniques." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726166.

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Kumar, Abhishek, Paras Jain, Priyank Pandey, Shyamalendu Das, Vishan Kumar Gupta, and Dhiraj Kumar Gupta. "House Price Prediction System Using Ensemble Learning Approach." In 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2024. https://doi.org/10.1109/pdgc64653.2024.10984370.

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Hamami, Faqih, and Iqbal Ahmad Dahlan. "Regression Modeling for House Price Prediction in Java Island." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10747910.

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Saefudin, M. Romi, Mindi Richia Putri, Abdul Hadi, Heri Wijayanto, and Budi Irmawati. "Significant Features for House Price Prediction Using Machine Learning." In 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). IEEE, 2024. https://doi.org/10.1109/comnetsat63286.2024.10862860.

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Sul, Astha, Vaishnavi Jagtap, Parantap Jesalpura, Anushka Nema, and Rajkumar R. "Optimizing House Price Prediction: Comparative Analysis of Machine Learning Techniques." In 2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceeict61591.2024.10718610.

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Reports on the topic "House Price Prediction"

1

Flemming, Jens. Predicting house prices (part 1). Westsächsische Hochschule Zwickau, 2021. http://dx.doi.org/10.25366/2021.91.

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Flemming, Jens. Predicting house prices (part 2). Westsächsische Hochschule Zwickau, 2021. http://dx.doi.org/10.25366/2021.92.

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