Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction

Abstract

In recent years several complaints about racial discrimination in appraising home values have been accumulating. For several decades, to estimate the sale price of the residential properties, appraisers have been walking through the properties, observing the property, collecting data, and making use of the hedonic pricing models. However, this method bears some costs and by nature is subjective and biased. To minimize human involvement and the biases in the real estate appraisals and boost the accuracy of the real estate market price prediction models, in this research we design data-efficient learning machines capable of learning and extracting the relation or patterns between the inputs (features for the house) and output (value of the houses). We compare the performance of some machine learning and deep learning algorithms, specifically artificial neural networks, random forest, and k nearest neighbor approaches to that of hedonic method on house price prediction in the city of Boulder, Colorado. Even though this study has been done over the houses in the city of Boulder it can be generalized to the housing market in any cities. The results indicate non-linear association between the dwelling features and dwelling prices. In light of these findings, this study demonstrates that random forest and artificial neural networks algorithms can be better alternatives over the hedonic regression analysis for prediction of the house prices in the city of Boulder, Colorado.

Investigating the Correlation between Air Pollution and Housing Prices in Seoul, South Korea: Application of Explainable Artificial Intelligence in Random Forest Machine Learning

“…It combines many decision trees using a bagging method called bootstrap aggregation [78]. This approach involves individually training several different trees and then aggregating the predictions of multiple decision trees, resulting in more reliable predictions than a single decision tree [29]. In addition, random forest algorithms such as decision trees can be very useful in housing value analysis because they can capture non-linear relationships between dependent and independent variables [79] and have the advantage of high model accuracy [80].…”

“…Among structural characteristics, area is one of the most commonly considered variables. Generally, it has been found that housing prices tend to be higher as the area increases [25][26][27][28][29][30], and similarly, higher floors are associated with higher housing prices [3,[31][32][33][34][35]. Additionally, factors such as the number of rooms and bathrooms are frequently taken into account, and overall, it has been observed that housing prices tend to be higher with a greater number of rooms and bathrooms [36][37][38][39][40].…”

“…Furthermore, as evidence continues to show that machine-learning methods outperform traditional hedonic models in terms of predictive power, studies utilizing machine learning to estimate housing prices are rapidly increasing. However, the use of artificial intelligence methods based on machine learning only provides conclusions, such as predictive power, based on its own judgment without revealing the mechanisms through which individual variables lead to these conclusions, which is a significant limitation [29,68,69].…”

Nonparametric prediction for univariate spatial data: Methods and applications

“…MAE = mean absolute error. Chica-Olmo, 2007;Chica-Olmo et al, 2019;Chica-Olmo & Cano-Guervos, 2020;Seya & Shiroi, 2022;Yazdani, 2021;Zaki et al, 2022). More recently, machine learning and deep learning methodologies have been used…”

The Effect of Outlier Detection Methods in Real Estate Valuation with Machine Learning

“…In parallel with all these developments, a wide variety of machine learning approaches have been used by many researchers in the task of residential real estate price prediction. Random Forest Yilmazer & Kocaman, 2020;Gupta et al, 2021;Tchuente & Nyawa, 2021;Bilgilioğlu & Yılmaz, 2021;Kim et al, 2021;Steurer et al, 2021;Yazdani, 2021;Imran et al , 2021;Truong et al, 2020;Ho et al, 2021;Bergadano et al, 2021;Jui et al, 2020;Fu, 2018;Alkan et al, 2022), Support Vector Regression (Yacim and Boshoff, 2020;Manasa et al, 2020;García-Magariño et al, 2020;Pai and Wang, 2020;Tchuente and Nyawa, 2021;Bilgilioğlu and Yılmaz, 2021;Imran et al, 2021;Chou et al, 2022 ;Ho et al, 2021;Alkan et al, 2022), Decision Trees (Sawant et al, 2018;Pérez-Rave et al, 2020;Pai and Wang, 2020;Alfaro-Navarro et al, 2020;Mrsic et al, 2020;Bilgilioğlu and Yılmaz, 2021;Sing et al , 2021;Sangha, 2021;Büyük and Ünel, 2021;Chou et al, 2022;Shi et al, 2022), Neural Networks (Štubňová et al, 2020;Yacim and Boshoff, 2020;Pai and Wang, 2020;…”