[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123447-en":3,"doc-seo-123447-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123447,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting Housing Prices in Sweden - A Comparative Study of Linear Regression and Machine Learning Models - research","Study of drivers behind Swedish housing asking prices using property attributes and pricing metrics, including rooms, land area, living area, and price per square meter. Linear regression is applied to quantify explanatory power and show significant positive coefficient relationships, accounting for 18.9% of the observed variation. To overcome linear-model limitations in capturing nonlinear interactions, decision trees and random forests are evaluated for generalization on test data. The decision tree overfits training performance, while the random forest achieves stronger predictive accuracy (test R² = 0.892) with low prediction errors. Diagnostic checks address multicollinearity and autocorrelation, and results inform model selection for investors and policymakers.","Predicting Housing Prices in Sweden: A Comparative Study of Linear Regression and Machine Learning Models  \nLuoping Feng  \nSchool of Data Science and Bigdata Technology, Hainan University, Haikou, China  \nAbstract. This study investigates the factors influencing the asking prices in the Swedish housing market, focusing on property-specific variables such as the number of rooms, land area, living area, and price per square meter.  \nUsing a linear regression model, the analysis reveals that these factors explain 18.9% of the variation in asking prices, with all coefficients showing significant positive relationships. To address the limitations of linear models, this study further employs nonlinear machine learning approaches, including decision trees and random forests, to capture complex interactions in the data.  \nThe decision tree model achieves perfect fit on training data (R² = 1.000) but shows reduced generalization on test data (R² = 0.800), suggesting potential overfitting. In contrast, the random forest model demonstrates robust performance, with high explanatory power (R² = 0.892 on test data) and minimal prediction errors, highlighting its superiority for housing price forecasting. Diagnostic tests confirm the absence of multicollinearity and autocorrelation in the linear model, while the machine learning models provide deeper insights into nonlinear relationships. These findings offer valuable guidance for investors and policymakers, emphasizing the importance of model selection in housing market analysis. Future research could integrate spatial analytics and macroeconomic shocks to further improve predictive accuracy.  \n1 Introduction  \nThe housing market plays a crucial role in modern economies, acting as both an essential part of financial markets and a key driver of national economic stability [1] . Housing prices are influenced by a complex interplay of factors, including macroeconomic conditions, social trends, and local market dynamics [2,3]. These factors make accurate predictions of housing prices vital for a variety of stakeholders, including policymakers, investors, and consumers. Governments, for instance, rely on housing price forecasts to stabilize real estate markets, preventing large fluctuations in prices, which can have detrimental effects on the broader economy [4] . This stability is essential for fostering long-term economic growth, as the housing sector directly affects employment, wealth distribution, and consumer spending.  \n[20223000406@hainanu.edu.cn](20223000406@hainanu.edu.cn)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nThe complexity of housing price prediction lies in the multifaceted nature of the housing market [5, 6] . Housing serves as both a consumption good (homes for living) and an investment good (assets), meaning that prices are influenced by both personal and economic factors. Demand-side factors include household income, population growth, and shifts in preferences, while supply-side factors, such as construction costs, land availability, and zoning laws, also play a significant role. Furthermore, macroeconomic variables like interest rates, inflation, and income inequality can significantly affect the affordability and desirability of housing.  \nTraditionally, econometric models such as linear regression and variance analysis have been used to predict housing prices [7] . Linear regression is particularly useful for understanding the relationship between housing prices and a set of independent variables. Through this method, it is possible to identify how changes in certain factors, such as location or square footage, influence housing prices. Variance analysis, on the other hand, allows the extent to which variations in housing prices are explained by the included factors t","cbCais8h0z4ar29z","https://ap.wps.com/l/cbCais8h0z4ar29z","pdf",331939,1,"English","en",105,"# Introduction\n## Housing price factors and forecasting importance\n## Linear econometric approaches and limitations\n## Machine learning alternatives\n## Study objective and dataset scope\n# Methodology\n## Data source and description","[{\"question\":\"Which property-specific variables are used to analyze Swedish asking prices?\",\"answer\":\"The study uses rooms, land area, living area, and price per square meter, along with other dataset features such as property type and location-related information.\"},{\"question\":\"How does linear regression perform in explaining asking price variation?\",\"answer\":\"Linear regression explains 18.9% of the variation in asking prices and all coefficients show significant positive relationships.\"},{\"question\":\"Why is the decision tree model considered problematic in this study?\",\"answer\":\"It achieves a perfect fit on training data (R² = 1.000) but drops to R² = 0.800 on test data, indicating potential overfitting.\"}]","Predicting Housing Prices in Sweden - A Comparative Study of Linear Regression and Machine Learning Models - research | PDF",1785816574,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"predicting-housing-prices-in-sweden-a-comparative-study-of-linear-regression-and-machine-learning-models-research","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/predicting-housing-prices-in-sweden-a-comparative-study-of-linear-regression-and-machine-learning-models-research/123447/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Which property-specific variables are used to analyze Swedish asking prices?","Question",{"text":74,"@type":75},"The study uses rooms, land area, living area, and price per square meter, along with other dataset features such as property type and location-related information.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does linear regression perform in explaining asking price variation?",{"text":79,"@type":75},"Linear regression explains 18.9% of the variation in asking prices and all coefficients show significant positive relationships.",{"name":81,"@type":72,"acceptedAnswer":82},"Why is the decision tree model considered problematic in this study?",{"text":83,"@type":75},"It achieves a perfect fit on training data (R² = 1.000) but drops to R² = 0.800 on test data, indicating potential overfitting.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]