[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125057-en":3,"doc-seo-125057-105":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},125057,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning for real estate valuation - Astana, Kazakhstan case","Machine learning models are investigated for forecasting and evaluating house prices using data scraped from real estate advertisements in Kazakhstan’s “sale of secondary housing” category on krisha.kz. The study examines key pricing factors including property quality, location, size, and building materials, and analyzes how strongly these factors correlate with predicted prices. Multiple regression, SVM, Gaussian process, tree-based ensembles, and Bayesian-regularized neural networks are compared using accuracy metrics. Bayesian regularization neural networks achieve the best performance (MSE 32.14, R 0.9899), supporting reliable apartment discovery and valuation.","Machine learning for real estate valuation: Astana,  \nKazakhstan case  \nAlibek Barlybayev1,2, Arman Sankibayev1, Rozamgul Niyazova1,2, Gulnara Akimbekova3  \n1Department of Artificial Intelligence Technologies, Faculty of information technologies, L.N. Gumilyov Eurasian National University,  \nAstana, Kazakhstan  \n2Higher School of Information Technology and Engineering, Astana International University, Astana, Kazakhstan 3Department of Microbiology and Virology named after. Sh.I. Sarbasova, Faculty of dentistry, Astana Medical University , Astana,  \nKazakhstan  \nArticle history:  \nReceived Jan 18, 2024 Revised Mar 16, 2024 Accepted Apr 6, 2024  \nKeywords:  \nMachine learning  \nNeural network  \nReal estate valuation criteria Real estate value forecasting Regression learner  \nCorresponding Author:  \nPurpose of this research is to investigate the accuracy of machine learning models in forecasting and evaluating house prices, and to understand the key factors that impact pricing. The study involved analyzing data scraped from real estate ads in the “sale of secondary housing” category on the website [krisha.kz. The paper](krisha.kz. The paper) emphasizes the importance of understanding the factors that affect house prices, such as quality, location, size, and building materials. It was concluded that these factors have a strong correlation with house price prediction. The information available on [krisha.kz](krisha.kz) was found tobe a useful resource for finding good apartments. The data collected by the scraper was analyzed by models: Linear regression (LR), interactions linear regression (ILR), robust linear regression (RLR), fine tree regression (FTR), medium tree regression (MTR), coarse tree regression (CTR), linear support vector machine (LSVM), quadratic SVM (QSVM), medium gaussian SVM (MGSVM), rational quadratic gaussian process regression (RQGPR), boosted trees (BoosT), bagged trees (BagT), neural network based on the bayesian regularization algorithm (BR-BPNN) . BR-BPNN showed better results than other models, with an MSE of 32.14 and R of 0.9899.  \nThis is an open access article under the CC BY-SA license.  \nAlibek Barlybayev  \nDepartment of Artificial Intelligence Technologies, Faculty of information technologies L.N. Gumilyov Eurasian National University  \nAstana, Kazakhstan  \nEmail: [frank-ab@mail.ru](frank-ab@mail.ru)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nOver the years, house price prediction has become a key research topic, as the demand for houses continues to sky-rocket. It is essential to develop a suitable framework that allows both buyers and sellers to make quick decisions when it comes to purchasing or selling property. Homebuyers want to be provided with comprehensive information in order to make decisions, but the volatile prices and high demand of real estate can make it difficult for them. Real estate valuations are often required for various investment purposes, such as providing collateral for secured loans from mortgage lenders. Such appraisals can provide valuable insights into the true worth of a property. Valuation reports are used for a variety of reasons like obtaining insurance premiums, determining rents, and assessing sales/purchase prices. Subsequently, it’s essential to get reliable methods, which can accurate valuation figures since a wrong investment valuation could be devastating for investors that need financial information. Predicting house prices with a predictive analytics system can offer invaluable assistance to a variety of stakeholders. Lahmiri et al. [1] investigates the  \neffectiveness of various machine learning algorithms in predicting housing prices. It found that some algorithms were notably more effective than others in this context. A significant discovery of the study is the role of Bayesian optimization in boosting the performance of these models. By fine-tuning the algorithms ’hyperparameters, the predictive accuracy of the models was enhanced, underscoring the cr","cbCaibSHFUa5iOJN","https://ap.wps.com/l/cbCaibSHFUa5iOJN","pdf",562156,1,12,"English","en",105,"# Abstract\n# Introduction\n## House price prediction needs and applications\n## Motivation for reliable valuation methods\n## Related work and model comparisons","[{\"question\":\"What is the main purpose of the study on house prices?\",\"answer\":\"To evaluate the accuracy of machine learning models for forecasting and assessing house prices and to identify key factors affecting pricing.\"},{\"question\":\"What data source is used for building the models?\",\"answer\":\"Data scraped from real estate ads in the “sale of secondary housing” category on the krisha.kz website.\"},{\"question\":\"Which model performs best and how is it measured?\",\"answer\":\"The Bayesian regularization neural network (BR-BPNN) delivers the best results, with MSE 32.14 and R 0.9899.\"}]","Machine learning for real estate valuation - Astana, Kazakhstan case | PDF",1785896395,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-real-estate-valuation-astana-kazakhstan-case","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-real-estate-valuation-astana-kazakhstan-case/125057/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main purpose of the study on house prices?","Question",{"text":75,"@type":76},"To evaluate the accuracy of machine learning models for forecasting and assessing house prices and to identify key factors affecting pricing.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data source is used for building the models?",{"text":80,"@type":76},"Data scraped from real estate ads in the “sale of secondary housing” category on the krisha.kz website.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best and how is it measured?",{"text":84,"@type":76},"The Bayesian regularization neural network (BR-BPNN) delivers the best results, with MSE 32.14 and R 0.9899.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]