Machine learning with explainability or spatial hedonics tools? An analysis of the asking prices in the housing market in Alicante, Spain

Rico-Juan, Juan Ramón; Paz, Paloma Taltavull de La

doi:10.1016/j.eswa.2021.114590

Cited by 68 publications

(55 citation statements)

References 52 publications

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“…Geographically, machine learning modeling has been applied for price-related forecasts in housing markets across many different countries and/or regions, including the USA (Nghiep and Al, 2001; Peterson and Flanagan, 2009; Park and Bae, 2015; Plakandaras et al. , 2015), China (Gu et al , 2011; Ho et al , 2021; Lam et al , 2008; Li et al , 2020; Liu and Liu, 2019; Xin and Runeson, 2004; Xu and Li, 2021), Spain (Rico-Juan and de La Paz, 2021), Italy (Morano and Tajani, 2013; Chiarazzo et al. , 2014; Morano et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…the gross national product, gross domestic product, stock market index, consumer price index, default rate, interest rate and unemployment) for valuations (Kang et al. , 2020), from prices themselves for technical forecasts (Gu et al , 2011; Li et al , 2020; Xin and Runeson, 2004), from house-related characteristics for technical forecasts (Chen et al , 2017; Embaye et al , 2021; Igbinosa, 2011; Kang et al , 2020; Kitapci et al , 2017; Lam et al , 2008; Liu and Liu, 2019; Morano and Tajani, 2013; Nghiep and Al, 2001; Park and Bae, 2015; Rico-Juan and de La Paz, 2021; Terregrossa and Ibadi, 2021; Yasnitsky et al , 2021) and from macroeconomics for technical forecasts (Azadeh et al , 2014; Kang et al , 2020; Lam et al , 2008; Liu and Liu, 2019; Plakandaras et al , 2015; Rico-Juan and de La Paz, 2021; Xin and Runeson, 2004; Yasnitsky et al , 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recently, different machine learning approaches and algorithms, including the nearest neighbor, random forest, regression tree, support vector regression, deep learning, ensemble learning, boosting and bagging, have been found to be attractive and useful tools to many forecasting problems related to prices in the housing market (e.g. Chen et al , 2017; Embaye et al , 2021; Gu et al , 2011; Ho et al , 2021; Li et al , 2020; Liu and Liu, 2019; Park and Bae, 2015; Plakandaras et al , 2015; Rico-Juan and de La Paz, 2021; Xu and Li, 2021; Xu and Zhang, 2021c, f). In particular, previous work has shown that the neural network model is promising to forecast financial and economic time series, which generally tend to be highly chaotic and noised (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Rent index forecasting through neural networks

Zhang

2021

JES

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PurposeChinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people, investors and policy makers. Here, the authors approach this issue by researching neural networks for rent index forecasting from 10 major cities for March 2012 to May 2020. The authors aim at building simple and accurate neural networks to contribute to pure technical forecasting of the Chinese rental housing market.Design/methodology/approachTo facilitate the analysis, the authors examine different model settings over the algorithm, delay, hidden neuron and data spitting ratio.FindingsThe authors reach a rather simple neural network with six delays and two hidden neurons, which leads to stable performance of 1.4% average relative root mean square error across the ten cities for the training, validation and testing phases.Originality/valueThe results might be used on a standalone basis or combined with fundamental forecasting to form perspectives of rent price trends and conduct policy analysis.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rent index forecasting through neural networks

Zhang

2021

JES

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“…Hu [35] monitored rental house prices with social media data, revealed the determinants and relative importance of rental house prices based on machine-learning approaches, and demonstrated the ability to integrate machine learning with the hedonic model to map spatial patterns. Rico-Juan [36] discovered that the methods of ordinary least squares hedonic regression, quantile hedonic regression, and machine learning have their respective superiorities in explaining housing prices, and the analysis of the Shapley values [37] based on random-forest machine learning is profound since it can identify the nonlinear and synergistic relationships from a three-dimensional perspective. These machine-learning approaches clearly have better accuracies than linear housing price models, and they also have a certain explanatory ability for the dependent variable.…”

Section: Nonlinear and Complex Housing Price Modelsmentioning

confidence: 99%

Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective

Shen

Lin

Zhu

et al. 2021

Land

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Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods can hardly predict very satisfactory prices, since the rental house prices involve both complicated nonlinear characteristics and spatial heterogeneity. The linear-based GWR model cannot characterize the nonlinear complexity of rental house prices, while existing deep-learning methods cannot explicitly model the spatial heterogeneity. This paper proposes a fully connected neural network–geographically weighted regression (FCNN–GWR) model that combines deep learning with GWR and can handle both of the problems above. In addition, when calculating the geographical location of a house, we propose a set of locational and neighborhood variables based on the quantities of nearby points of interests (POIs). Compared with traditional locational and neighborhood variables, the proposed “quantity-based” locational and neighborhood variables can cover more geographic objects and reflect the locational characteristics of a house from a comprehensive geographical perspective. Taking four major Chinese cities (Wuhan, Nanjing, Beijing, and Xi’an) as study areas, we compare the proposed method with other commonly used methods, and this paper presents a more precise estimation model for rental house prices. The method proposed in this paper may serve as a useful reference for individuals and enterprises in their transactions relevant to rental houses, and for the government in terms of the policies and positions of public rental housing.

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“…Other commonly used ML approaches are Decision Tree (DT) [12][13][14], Random Forest (RF) [15][16][17], Support Vector Machine (SVM) [18][19][20][21][22], Gradient Boosting Machine (GBM) [23][24][25][26], K Nearest Neighbors (KNN) [27][28][29][30], and Artificial Neural Network (ANN) [31][32][33][34]. Recently, Successful ML models using out-of-sample data set over predicted housing pricing, including support vector regression, regression tree, random forecast, bagging, boosting, Ridge and Lasso, and ensemble learning, have been discovered to be more efficient and realistic [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices

Khosravi¹,

Arif²,

Ghaseminejad³

et al. 2022

Preprint

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Real estate market analysis and place-based decision-making can both benefit from understanding house price development. Although considerable amounts of interest have been devoted to housing price modelling, the assessment of house price fluctuation still requires further comparing studies. Housing price prediction is challenging as contributing factors are quite dynamic and subject to a variety of regulating elements. The future understanding of the housing market trends not only provides sufficient customers’ investment trust potential but also enables the financial support to progress more realistic in advance. In this study, a comprehensive data-informed framework is developed to investigate and anticipate real estate house prices using historical data by combining explanatory features. We examined about 500 houses in the Boston area as a case study and discussed how the increase in housing prices could vary by each of the contributing components. Fourteen Machine Learning (ML) regressors imply to the dataset and lead to a comparative study of the accuracy of all the models. ML-based regressors forecast real estate home prices as a function of thirteen influencing factors. The most informative features were also selected by conducting the Permutation Feature Importance technique on all the features The study provides a comprehensive tool for evaluating the robustness and efficiency of ML models for housing price predictions. The results highlighted Random Forest as the best model has an R2 equals to 0.88 and Voting Regressor as the second highest rated model has R2 equals to 0.87. Results of multivariate exploratory data analysis also implied that the average number of rooms and percentage of the lower status of the population have the most significant impact on the price range predictions.

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Machine learning with explainability or spatial hedonics tools? An analysis of the asking prices in the housing market in Alicante, Spain

Cited by 68 publications

References 52 publications

Rent index forecasting through neural networks

Rent index forecasting through neural networks

Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective

Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices

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