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In recent years several complaints about racial discrimination in appraisinghome values have been accumulating. For several decades, to estimate the saleprice of the residential properties, appraisers have been walking through theproperties, observing the property, collecting data, and making use of thehedonic pricing models. However, this method bears some costs and by nature issubjective and biased. To minimize human involvement and the biases in the realestate appraisals and boost the accuracy of the real estate market priceprediction models, in this research we design data-efficient learning machinescapable of learning and extracting the relation or patterns between the inputs(features for the house) and output (value of the houses). We compare theperformance of some machine learning and deep learning algorithms, specificallyartificial neural networks, random forest, and k nearest neighbor approaches tothat 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 ofBoulder it can be generalized to the housing market in any cities. The resultsindicate non-linear association between the dwelling features and dwellingprices. In light of these findings, this study demonstrates that random forestand artificial neural networks algorithms can be better alternatives over thehedonic regression analysis for prediction of the house prices in the city ofBoulder, Colorado.

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