Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data

2015 ◽  
Vol 42 (6) ◽  
pp. 2928-2934 ◽  
Author(s):  
Byeonghwa Park ◽  
Jae Kwon Bae
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Fairfax County ◽  
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 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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