{"title":"Combining machine learning and econometrics: Application to commercial real estate prices","authors":"Marc K. Francke, Alex van de Minne","doi":"10.1111/1540-6229.12483","DOIUrl":null,"url":null,"abstract":"In this article, we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will be used to estimate a common trend, property type trends, location value, and property random effects for properties that sold more than once. The machine learning algorithm will fit the observed characteristics (features) in a complex nonlinear fashion. The model is applied to a small sample of 2652 transactions in Phoenix (AZ) between 2001 and 2021. We only observe a limited number of property characteristics. The average out‐of‐sample MAPE is below 11%, which is as good or even better compared to the average appraisal error found in literature. The out‐of‐sample MAPE is even 9% for properties that sold more than once in the training set. In addition, our model provides indexes and locational heatmaps. These have their own uses and cannot be obtained with standard machine learning algorithms.","PeriodicalId":47731,"journal":{"name":"Real Estate Economics","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Real Estate Economics","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1111/1540-6229.12483","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
In this article, we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will be used to estimate a common trend, property type trends, location value, and property random effects for properties that sold more than once. The machine learning algorithm will fit the observed characteristics (features) in a complex nonlinear fashion. The model is applied to a small sample of 2652 transactions in Phoenix (AZ) between 2001 and 2021. We only observe a limited number of property characteristics. The average out‐of‐sample MAPE is below 11%, which is as good or even better compared to the average appraisal error found in literature. The out‐of‐sample MAPE is even 9% for properties that sold more than once in the training set. In addition, our model provides indexes and locational heatmaps. These have their own uses and cannot be obtained with standard machine learning algorithms.
期刊介绍:
As the official journal of the American Real Estate and Urban Economics Association, Real Estate Economics is the premier journal on real estate topics. Since 1973, Real Estate Economics has been facilitating communication among academic researchers and industry professionals and improving the analysis of real estate decisions. Articles span a wide range of issues, from tax rules to brokers" commissions to corporate real estate including housing and urban economics, and the financial economics of real estate development and investment.