Housing price prediction: parametric versus semi-parametric spatial hedonic models

2017 ◽  
Vol 20 (1) ◽  
pp. 27-55 ◽  
Author(s):  
José-María Montero ◽  
Román Mínguez ◽  
Gema Fernández-Avilés
Keyword(s):  
Housing Price ◽  
Hedonic Models ◽  
Price Prediction ◽  
Spatial Hedonic Models

scholarly journals Erratum to: Housing price prediction: parametric versus semi-parametric spatial hedonic models

2017 ◽  
Vol 20 (1) ◽  
pp. 107-109
Author(s):  
José-María Montero ◽  
Román Mínguez ◽  
Gema Fernández-Avilés
Keyword(s):  
Housing Price ◽  
Hedonic Models ◽  
Price Prediction ◽  
Spatial Hedonic Models

Spatial hedonic models for environmental hazards and health outcomes

2020 ◽  
Vol 52 (35) ◽  
pp. 3830-3841
Author(s):  
Theodoros Makridakis ◽  
Sotiris Karkalakos
Keyword(s):  
Health Outcomes ◽  
Environmental Hazards ◽  
Hedonic Models ◽  
Spatial Hedonic Models

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 Based on Multiple Linear Regression

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qingqi Zhang
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Housing Prices ◽  
Housing Price ◽  
Multiple Linear Regression Model ◽  
Data Set ◽  
Factors Affecting ◽  
Price Prediction

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.

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