[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127920-en":3,"doc-seo-127920-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127920,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Improving Real Estate Investment Trusts (REITs) - time-series prediction accuracy using machine learning and technical analysis indicators","The study addresses investor needs for better returns with lower risk when including Real Estate Investment Trusts (REITs) in diversified portfolios by improving future price forecasting. Five machine learning models are trained on historical REIT prices augmented with technical analysis indicators, then evaluated against statistical benchmarks such as Holt’s method, TBATS, and ARIMA. Results show reduced prediction errors, up to 60% in some cases, and stronger multi-asset portfolio performance. SHAP and SAGE analyses further confirm the added value of technical indicators.","Improving Real Estate Investment Trusts (REITs) time-series prediction accuracy using machine learning and technical analysis indicators  \nFatim Z. Habbab1 · Michael Kampouridis1 · Tasos Papastylianou2  \nAccepted: 18 November 2024 © The Author(s) 2024  \nAbstract  \nThe primary goal of investors who include Real Estate Investment Trusts (REITs) in their portfolios is to achieve better returns while reducing the overall risk of their investments. REITs are entities responsible for owning and managing real estate properties. To achieve greater returns while reducing risk, it is essential to accurately predict future REIT prices. This study explores the predictive capability of five different machine learning algorithms used to predict REIT prices. These algorithms include Ordinary Least Squares Linear Regression, Support Vector Regression, k-Nearest Neighbours Regression, Extreme Gradient Boosting, and Long/Short-Term Memory Neural Networks. Additionally, historical REIT prices are supplemented with Technical Analysis indicators (TAIs) to aid in price predictions. While TA indicators are commonly used in stock market forecasting, their application in the context of REITs has remained relatively unexplored. The study applied these algorithms to predict future prices for 30 REITs from the United States, United Kingdom, and Australia, along with 30 stocks and 30 bonds. After obtaining our price predictions, we employ a Genetic Algorithm (GA) to optimise weights of a diversified portfolio. Our results reveal several key findings: (i) all machine learning algorithms demonstrated low average and standard deviation values in the error rate distributions, outperforming commonly used statistical benchmarks such as Holt’s Linear Trend Method (HLTM), Trigonometric Box-Cox Autoregressive Time Series (TBATS), and Autoregressive Integrated Moving Average (ARIMA); (ii) incorporating Technical Analysis indicators in the ML algorithms resulted in a significant reduction in prediction errors, up to 60% in some cases; and (iii) a multi-asset portfolio constructed using predictions that incorporated Technical Analysis indicators outperformed a portfolio based solely on predictions derived from past prices. Furthermore, this study employed Shapley Value-based techniques, specifically SHAP and SAGE, to analyse the importance of the features used in the analysis. These techniques provided additional evidence of the value added by Technical Analysis indicators in this context.  \nKeywords Machine learning · REITs · Financial time-series · Technical analysis  \nExtended author information available on the last page of the article  \n1 3  \n1 Introduction  \nThe optimisation of a portfolio that includes real estate is an important area of research in finance (Thakkar and Chaudhari 2021) . Real estate has gained significant interest from investors globally due to its potential to enhance returns and reduce risks in mixed-asset portfolios (Habbab et al. 2022). Empirical evidence suggests that including real estate assets in a portfolio can improve risk-adjusted returns. For example, research studies have demonstrated that real estate investments tend to have low correlation with other asset classes such as stocks and bonds, providing diversification benefits (Gatzlaff and Geltner 1991) . Furthermore, real estate investments have been shown to be an effective hedge against inflation, as they tend to maintain their value or even appreciate during inflationary periods (Miles 2004) .  \nPrevious studies have recommended allocating a portion of the portfolio to real estate between 10% and 15%, with longer holding periods potentially increasing this allocation (Delfim and Hoesli 2019, 2020) . However, in order to produce the requisite models for estimating asset returns and volatility in real estate investments, such studies have traditionally depended on appropriate historical data being available; this creates a challenge, as the nature of real estate markets, which a","cbCaitJTbh3BhOYA","https://ap.wps.com/l/cbCaitJTbh3BhOYA","pdf",9506372,6,1,47,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What is the primary objective of the study on REIT forecasting?\",\"answer\":\"To improve prediction accuracy of future REIT prices so investors can achieve better returns while reducing overall investment risk.\"},{\"question\":\"Which machine learning methods are used to predict REIT prices?\",\"answer\":\"The study evaluates Ordinary Least Squares Linear Regression, Support Vector Regression, k-Nearest Neighbours Regression, Extreme Gradient Boosting, and Long/Short-Term Memory Neural Networks.\"},{\"question\":\"How do technical analysis indicators affect prediction and portfolio results?\",\"answer\":\"Adding technical analysis indicators as features significantly reduces prediction errors (up to 60% in some cases) and leads to stronger portfolio performance compared with using predictions based only on past prices.\"}]","Improving Real Estate Investment Trusts (REITs) - time-series prediction accuracy using machine learning and technical analysis indicators | PDF",1785942961,118,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"improving-real-estate-investment-trusts-reits-time-series-prediction-accuracy-using-machine-learning-and-technical-analysis-indicators","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/improving-real-estate-investment-trusts-reits-time-series-prediction-accuracy-using-machine-learning-and-technical-analysis-indicators/127920/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the primary objective of the study on REIT forecasting?","Question",{"text":77,"@type":78},"To improve prediction accuracy of future REIT prices so investors can achieve better returns while reducing overall investment risk.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which machine learning methods are used to predict REIT prices?",{"text":82,"@type":78},"The study evaluates Ordinary Least Squares Linear Regression, Support Vector Regression, k-Nearest Neighbours Regression, Extreme Gradient Boosting, and Long/Short-Term Memory Neural Networks.",{"name":84,"@type":75,"acceptedAnswer":85},"How do technical analysis indicators affect prediction and portfolio results?",{"text":86,"@type":78},"Adding technical analysis indicators as features significantly reduces prediction errors (up to 60% in some cases) and leads to stronger portfolio performance compared with using predictions based only on past prices.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]