House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach

Yazdani, Mahdieh

doi:10.48550/arxiv.2108.02442

Cited by 2 publications

(3 citation statements)

References 53 publications

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“…For example, Rosen (1974) used the hedonic regression models in the field of real estate and urban economics. Thereafter, residential hedonic analysis has become widely used as an assessment tool to model real estate markets (see e.g., McMillan et al (1980), Blomquist and Worley (1981), Milon et al (1984), Haase et al (2013), Jiang et al (2014), Del Giudice et al (2017, Król (2020), andYazdani (2021)). In the housing market the HP regression model is essentially a function of a bundle of property characteristics such as structural attributes, socio-economic status of the neighborhood, environmental amenities, and location.…”

Section: Hedonic Pricing Modelmentioning

confidence: 99%

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

Yazdani

2021

Preprint

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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.

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Section: Hedonic Pricing Modelmentioning

confidence: 99%

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

Yazdani

2021

Preprint

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“…The results of the analysis are expected to be able to group into different categories so people that want to buy houses could have enough information in buying a house that match their budget and criteria. There is already previous research that did a house prices segmentation using hedonic regression [1].…”

Section: Introductionmentioning

confidence: 99%

“…The result of the research can be seen on Table 2. The are two goals of this research: (1). Identify how to optimize the parameters of GMM, (2).…”

Section: Introductionmentioning

confidence: 99%

House Prices Segmentation Using Gaussian Mixture Model-Based Clustering

Raditya¹,

Indwiarti²,

Rohmawati³

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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House is a place for humans to live and a main necessity for humans. For years, the need for houses is increasing and varied so that it affects the selling price of the house. Therefore, more research is needed to learn about the selling price of houses. This research is only focusing on house price segmentation in DKI Jakarta using the Gaussian Mixture Model-Based Clustering Method with the Expectation-Maximization algorithm. The goal of this research is to make a house price segmentation model so that we can obtain useful information for the potential buyer. Clustering with GMM utilize the log-likelihood function to optimize the GMM parameters. The result of this research is houses in DKI Jakarta can be segmented into 3 different clusters. The first cluster is for the low-profile houses. The second cluster is for the mid-profile houses. The third cluster is for the high-profile houses. The silhouette score that was produced by the clustering method is 0.60866 meaning that this score is quite good because it’s close to a value of 1.

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House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach

Cited by 2 publications

References 53 publications

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

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

House Prices Segmentation Using Gaussian Mixture Model-Based Clustering

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