[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119361-en":3,"doc-seo-119361-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119361,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Testing machine learning systems in real estate","Uncertainty about how machine learning (ML) models produce estimates limits practical deployment in real estate markets. The article addresses how ML systems reach predictions when model transparency is limited and how practitioners can ensure compliance with legal and ethical requirements. It proposes a dedicated software testing framework for applied ML systems and demonstrates how system testing can confirm intended behavior. Two system-testing procedures for ML image classifiers used in automated valuation models (AVMs) illustrate the approach.","DOI: 10.1111/1540-6229.12416  \nORIGINAL ARTICLE  \nTesting machine learning systems in real estate  \nWayne Xinwei Wan1   Thies Lindenthal2   \n1 Department of Banking and Finance, Monash University, Clayton, Victoria, Australia  \n2 Department of Land Economy, The University of Cambridge, Cambridge, Cambridgeshire, UK  \nCorrespondence  \nWayne Xinwei Wan, Department of Banking and Finance, Monash University, W1025 Menzies Bldg, Wellington Rd, Clayton, VIC 3800, Australia. [Email: wayne.wan@monash.edu](Email: wayne.wan@monash.edu)  \nAbstract  \nUncertainty about the inner workings of machine learning (ML) models holds back the application of MLenabled systems in real estate markets. How do ML models arrive at their estimates? Given the lack of model transparency, how can practitioners guarantee that ML systems do not run afoul of the law? This article first advocates a dedicated software testing framework for applied ML systems, as commonly found in computer science. Second, it demonstrates how system testing can verify that applied ML models indeed perform as intended. Two system-testing procedures developed for ML image classifiers used in automated valuation models (AVMs) illustrate the approach.  \nKEYWORDS  \naccountability gap, computer vision, explainable machine learning, real estate, system testing  \n1  INTRODUCTION  \nThe black-box nature of machine learning (ML) techniques poses a risk to businesses developing ML-enabled systems: How can they verify that a system is performing in thewayit is meant to perform? Can they ensure that its outcomes are not spurious, biased, or unlawful? Still, ML-enabled systems continue to reshape commerce, personal interactions, entertainment, medicine, government services, state supervision—and research (Simester et al., 2020) . In real estate and urban studies, a rapidly expanding literature explores the potential of ML algorithms, introducing novel measurements of the physical environments or using these estimates to improve the traditional real estate valuation and urban planning processes (e.g., Glaeser et al., 2018; Johnson et al., 2020; Karimi et al., 2019; Lindenthal & Johnson, 2021; Liu et al., 2017; Rossetti et al., 2019; Schmidt &  \n© 2022 American Real Estate and Urban Economics Association.  \nLindenthal, 2020; Shen & Ross, 2021). These studies, again and again, demonstrate the undisputed power of ML systems as prediction machines. Still, it remains difficult for researchers to establish causality or for end users to understand the internal workings of any models. An “accountability gap”(Adadi & Berrada, 2018) remains: How do the models arrive at their prediction results? Can we trust them not to bend rules or to cut corners?  \nThis accountability gap holds back the deployment of ML-enabled systems in real-life situations (Ibrahim et al., 2020). If engineers cannot observe the inner workings ofthe models, how can they guarantee reliable outcomes? Furthermore, the accountability gap also leads to obvious dangers: Flaws in prediction machines are not easily discernible by classic cross-validation approaches (Ribeiro et al., 2016) . More importantly, the opacity of the ML models also gives rise to the legal and ethical concerns for its real-life applications (Mullainathan & Obermeyer, 2017). For instance, anecdotal evidence reports that some ML engines for recruitment have exerted biases again the female applicants (Dastin, 2018) . Traditional ML model validation metrics such as the magnitude of prediction errors or 􀀂1-scores can evaluate the models’predictive performance, but they provide limited insights for addressing the accountability gap.  \nTraining ML models is a software development process at heart. We therefore suggest that ML system developers follow best practices and industry standards in software testing. Particularly, the system-testing stage of software test regimes is essential: It verifies whether an integrated system performs the exact function as required in the init","cbCaipkd51JVasVY","https://ap.wps.com/l/cbCaipkd51JVasVY","pdf",1276991,1,25,"English","en",105,"# Introduction\n## Accountability gap in real estate ML\n## System testing framework for applied ML\n## Local model explanations and scalability\n## Use cases: image classifiers and AVMs","[{\"question\":\"Why does the “accountability gap” hinder real estate ML deployment?\",\"answer\":\"Because end users and practitioners cannot observe how models generate predictions, making it hard to judge whether outcomes are reliable, unbiased, or unlawful.\"},{\"question\":\"What solution does the article propose for testing applied ML systems?\",\"answer\":\"It advocates a dedicated software testing framework, emphasizing system testing to verify that integrated ML systems perform exactly as specified.\"},{\"question\":\"How do the proposed procedures relate to automated valuation models (AVMs)?\",\"answer\":\"They provide system-testing procedures for ML image classifiers used within AVMs, showing how testing can verify that models work as intended.\"}]","Testing machine learning systems in real estate | 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