Metrics for evaluating the performance of machine learning based automated valuation models

Steurer, Miriam; Hill, Robert J.; Pfeifer, Norbert

doi:10.1080/09599916.2020.1858937

Cited by 82 publications

(36 citation statements)

References 49 publications

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“…The general expression for R 2 (R-squared) is given in Equation ( 17). The R 2 metric is also called coefficient of determination [30]. This metric describes the relationship between actual results and predicted results.…”

Section: Performance Metricsmentioning

confidence: 99%

Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model

Mısır

Akar

2022

Mathematics

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Efficiency mapping has an important place in examining the maximum efficiency distribution as well as the energy consumption of designed electric motors at maximum torque and speed. Performing analysis at all operating points with FEM analysis in the motor design process requires high processing costs and time. In this article, a machine learning-based multivariate polynomial regression estimation model was developed to overcome these costly processes from FEM analysis. With the proposed method, the operating points of the motors in different conditions during the design process can be predicted in advance with high accuracy. In the study, two different models are developed for efficiency map and core loss estimation of interior permanent magnet synchronous motor design. The developed models use few parameters and predict with high accuracy. Estimation models shorten the design process and offer a less complex model. Obtained results are validated by comparison with FEM analysis.

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Section: Performance Metricsmentioning

confidence: 99%

Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model

Mısır

Akar

2022

Mathematics

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“…Metrics to measure the performance of real estate valuation models are also a major topic of discussion since each metric highlights a particular aspect of a model's performance. Steurer et al suggested ways to assess the effectiveness of several metrics in measuring the performance of machine learning models for automated valuation [15]. A total of 48 metrics were divided into seven classes, and seven final metrics (one per each group) were determined to be the most effective to evaluate automated valuation models.…”

Section: Fig 4 Apartment Floor and Building Floors Frequencymentioning

confidence: 99%

Automated Real Estate Valuation With Machine Learning: A Case Study on Apartment Sales in Yerevan

Sergoyan

Bezirganyan

2022

jaer

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Real estate is one of the major sectors of the Armenian economy and has been developing dynamically since Armenia transitions from planned to market economies in early 1990s. More recently, large online platforms have been developed in Armenia to advertise real estate offerings, thus reducing information asymmetry, and increasing liquidity in both sales and rental markets. Simultaneously, granular geospatial data became increasingly affordable via platforms such as OpenStreetMap, Google Maps and Yandex Maps. With granular data concerning a representative portion of the real estate offering available online, it is increasingly tenable to monitor the real estate market in real time and develop analytical tools that can automatically and accurately estimate the value of real estate assets based on their internal and external features. This paper sets out to analyze Armenia real estate market and assess the performance of a special class of machine learning models while predicting the price of a square meter of apartments in Yerevan. Furthermore, it is presented the way to determine the most decisive factors which have an influence on the price of apartments on sale.

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“…Steurer et al [51] performed a comparative study of seven metrics among all 48 possible various metrics to evaluate the Automated Valuation Models (AVMs) based on ML algorithms for house pricing prediction. Peng et al [52] provide a new method as the first life-long property valuation prediction model which is automated by using a Long Short-Term Memory (LSTM) network to model the temporal relationship for property transaction data across time after first using a Graph Convolutional Network (GCN) to extract the geographical information.…”

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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Metrics for evaluating the performance of machine learning based automated valuation models

Cited by 82 publications

References 49 publications

Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model

Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model

Automated Real Estate Valuation With Machine Learning: A Case Study on Apartment Sales in Yerevan

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

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