A Study on Housing Price Prediction Using Machine Learning

2021 ◽  
Vol 24 ◽  
pp. 223-243
Author(s):  
Hae-Jung Chun ◽  
Keyword(s):  
Machine Learning ◽  
Housing Price ◽  
Price Prediction

scholarly journals Machine Learning Housing Price Prediction in Petaling Jaya, Selangor, Malaysia

2019 ◽  
Vol 8 (2S11) ◽  
pp. 542-546
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Selling Price ◽  
Prediction Problem ◽  
Real Dataset ◽  
Price Prediction

This paper demonstrates the utilization of machine learning algorithms in the prediction of housing selling prices on real dataset collected from the Petaling Jaya area, Selangor, Malaysia. To date, literature about research on machine learning prediction of housing selling price in Malaysia is scarce. This paper provides a brief review of the existing machine learning algorithms for the prediction problem and presents the characteristics of the collected datasets with different groups of feature selection. The findings indicate that using irrelevant features from the dataset can decrease the accuracy of the prediction models.

scholarly journals Housing-Price Prediction in Colombia using Machine Learning

2021 ◽  
Author(s):  
MIGUEL ANGEL CORREA MANRIQUE ◽  
Omar Becerra Sierra ◽  
Daniel Otero Gomez ◽  
Henry Laniado ◽  
Rafael Mateus C ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evaluation Studies ◽  
Housing Prices ◽  
House Price ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Learning Tools ◽  
Price Prediction ◽  
Statistical Indicator ◽  
Real Estate Company

It is a common practice to price a house without proper evaluation studies being performed for assurance. That is why the purpose of this study provide an explanatory model by establishing parameters for accuracy in interpretation and projection of housing prices. In addition, it is intentioned to establish proper data preprocessing practices in order to increase the accuracy of machine learning algorithms. Indeed, according to our literature review, there are few articles and reports on the use of Machine Learning tools for the prediction of property prices in Colombia. The dataset in which the research is built upon was provided by an existing real estate company. It contains near 940,000 items (housing advertisements) posted on the platform from the year 2018 to 2020. The database was enriched using statistical imputation techniques. Housing prices prediction was performed using Decision Tree Regressors and LightGBM methods, thus deriving in better alternatives for house price prediction in Colombia. Moreover, to measure the accuracy of the proposed models, the Root Mean Squared Logarithmic Error (RMSLE) statistical indicator was used. The best cross validation results obtained were 0.25354±0.00699 for the LightGBM, 0.25296 ±0.00511 for the Bagging Regressor, and 0.25312±0.00559 for the ExtraTree Regressor with Bagging Regressor, and it was not found a statistical difference between their performances.

scholarly journals Housing Price Prediction via Improved Machine Learning Techniques

2020 ◽  
Vol 174 ◽  
pp. 433-442
Author(s):  
Quang Truong ◽  
Minh Nguyen ◽  
Hy Dang ◽  
Bo Mei
Keyword(s):  
Machine Learning ◽  
Housing Price ◽  
Machine Learning Techniques ◽  
Price Prediction ◽  
Learning Techniques

scholarly journals CAN MACHINE LEARNING ALGORITHMS ASSOCIATED WITH TEXT MINING FROM INTERNET DATA IMPROVE HOUSING PRICE PREDICTION PERFORMANCE?

2020 ◽  
Vol 24 (5) ◽  
pp. 300-312
Author(s):  
Jian-qiang Guo ◽  
Shu-hen Chiang ◽  
Min Liu ◽  
Chi-Chun Yang ◽  
Kai-yi Guo
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
House Prices ◽  
Learning Algorithms ◽  
Housing Prices ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Prediction Ability ◽  
Price Prediction ◽  
Search Data

Housing frenzies in China have attracted widespread global attention over the past few years, but the key is how to more accurately forecast housing prices in order to establish an effective real estate policy. Based on the ubiquitousness and immediacy of Internet data, this research adopts a broader version of text mining to search for keywords in relation to housing prices and then evaluates the predictive abilities using machine learning algorithms. Our findings indicate that this new method, especially random forest, not only detects turning points, but also offers prediction ability that clearly outperforms traditional regression analysis. Overall, the prediction based on online search data through a machine learning mechanism helps us better understand the trends of house prices in China.

scholarly journals Crop price prediction using supervised machine learning algorithms

2021 ◽  
Vol 1916 (1) ◽  
pp. 012042
Author(s):  
Ranjani Dhanapal ◽  
A AjanRaj ◽  
S Balavinayagapragathish ◽  
J Balaji
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Price Prediction
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