Recent trends in real estate research: a comparison of recent working papers and publications using machine learning algorithms

Breuer, Wolfgang; Steininger, Bertram I.

doi:10.1007/s11573-020-01005-w

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…It is also notable that the amount of research on predicting house prices and rents with ML is increasing. Explanations for this increase in research for prediction with ML in real estate valuation are increasing computing power, digitization and, in general, the incorporation of ML into new application domains (Breuer and Steininger, 2020; Piegeler and Bauer, 2021). After all, ML models may offer advantages, in many economic applications, over classic LR.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Due to increasing computing power and digitization, the incorporation of machine learning (ML) models is taking place in many domains, including real estate valuation (Breuer and Steininger, 2020; Piegeler and Bauer, 2021). With these models, it is possible to make more accurate predictions than with traditional OLS regressions (Valier, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Predictability of Belgian residential real estate rents using tree-based ML models and IML techniques

Lenaers

Boudt

Moor

2023

IJHMA

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Purpose The purpose is twofold. First, this study aims to establish that black box tree-based machine learning (ML) models have better predictive performance than a standard linear regression (LR) hedonic model for rent prediction. Second, it shows the added value of analyzing tree-based ML models with interpretable machine learning (IML) techniques. Design/methodology/approach Data on Belgian residential rental properties were collected. Tree-based ML models, random forest regression and eXtreme gradient boosting regression were applied to derive rent prediction models to compare predictive performance with a LR model. Interpretations of the tree-based models regarding important factors in predicting rent were made using SHapley Additive exPlanations (SHAP) feature importance (FI) plots and SHAP summary plots. Findings Results indicate that tree-based models perform better than a LR model for Belgian residential rent prediction. The SHAP FI plots agree that asking price, cadastral income, surface livable, number of bedrooms, number of bathrooms and variables measuring the proximity to points of interest are dominant predictors. The direction of relationships between rent and its factors is determined with SHAP summary plots. In addition to linear relationships, it emerges that nonlinear relationships exist. Originality/value Rent prediction using ML is relatively less studied than house price prediction. In addition, studying prediction models using IML techniques is relatively new in real estate economics. Moreover, to the best of the authors’ knowledge, this study is the first to derive insights of driving determinants of predicted rents from SHAP FI and SHAP summary plots.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictability of Belgian residential real estate rents using tree-based ML models and IML techniques

Lenaers

Boudt

Moor

2023

IJHMA

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“…However, given that Switzerland is home to many small and medium enterprises as well as multinational corporations, and that Switzerland's government places a high value on education, research, and innovation, this analysis could have some merit regarding whether companies are demanding sustainability transformation in real estate and whether the workforce is ready to provide and enable this [73].…”

Section: Limitationsmentioning

confidence: 99%

An Analysis of Sustainability in Real Estate in Job Advertisements and Personal Profiles in Switzerland

Wrase

2023

Sustainability

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Organizations in and associated with the real estate sector rely on a competent workforce capable of effectively managing sustainability practices. This study examined the alignment of sustainability-related qualifications between employers and individuals in the Swiss real estate-related job market. A comprehensive analysis was conducted on 600 job advertisements and 1520 personal profiles, employing a keyword-based search approach derived from sustainability definitions and contexts. The findings revealed that companies emphasized the importance of “sustainability” in their job advertisements, whereas employees rarely mentioned it in their profiles. Nevertheless, both employers and workers demonstrate a demand for expertise, competencies, and skills that contribute to fostering sustainability in the real estate domain. Noteworthy keywords encompassed digitalization, green buildings, economic thinking, creativity, and collaboration. To facilitate improved job matching between organizations and applicants and to ensure sustainable practices in the real estate sector, it is recommended that organizations integrate these suggested keywords in their job advertisements and that employees incorporate the corresponding keywords into their profiles. Furthermore, educational institutions can enhance their programs by incorporating these keywords and the proposed dimensions of sustainability into their educational frameworks.

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“…The analysis of homebuyers' litigation-related case decisions also suggests increasing complaints against builders/promoters and real estate agents for violations of agreements, construction defects, and service de ciencies. Authors with year Important features studied Gastanaga et al (1998), Berry, et al (2001), Asiedu (2002), Asiedu and Lien (2010), Büthe and Milner (2008), Mlachila andTakebe (2011), Ramsey-Musolf (2016), Glaeser (2017), Goering and Whitehead (2017), Biyase and Rooderick (2018), González-Val (2020) Policy issues and measures: Austerity measures, FDI, Financial crisis, Housing equity, Regulatory governance, Taxation issues Gibb and Hoesli (2003), Stone (2006), Gabriel (2010), Bramley (2011S), Zavei and Jusan (2012), Levitin and Wachter (2012), Singh (2013), Mulliner and Maliene (2015), Roy (2018), Kusiak (2019), Li and Chau (2019), Marsha and Stephen (2019), Haffner and Hulse (2019), Robin (2019), Breuer and Steininger (2020), Malik et al (2020) Socio-economic features: Builders' disparity treatment/ harassment, Corruption in property, Demand-supply issues, Housing affordability, Housing market assessment, Housing pricing and/ bubble formation, Poverty and housing needs, Public housing system and Mortgage instruments, Real estate business practices, User's housing motivation,…”

Section: Real Estate Market-indian Contextmentioning

confidence: 99%

The Indian real estate regulations and urban homebuyers: impact assessment

Chawla¹,

Kumar²

2022

Preprint

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The previously unregulated urban housing and real estate market scenario began to change by promulgating a new law, i.e., the Indian Real Estate (Regulation and Development) Act, 2016 (RERDA, 2016/Act). Claims are that the Act has been saving homebuyers from exploitation by the promoters' evasive and aggressive approach and impacting the economy and society. The paper briefly examines relevant provisions of the Act and judicial pronouncements protecting urban homebuyers' interests; it analyzes in-depth their impact on them in particular and society by empirically studying 540 sample homebuyers' reactions. Besides, the study discusses the implications of regulating foreign direct investments in aiding current policy perceptions supporting housing vis-a-vis the economy. The results show that very often, builders exploit the homebuyers. Timely judiciary interventions have removed the initial bottleneck created by builders, positively impacting builders' and governments' safeguarding of homebuyers' interests. Even four years after the Act's implementation, its implementation remains uneven.

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Recent trends in real estate research: a comparison of recent working papers and publications using machine learning algorithms

Cited by 9 publications

References 6 publications

Predictability of Belgian residential real estate rents using tree-based ML models and IML techniques

Predictability of Belgian residential real estate rents using tree-based ML models and IML techniques

An Analysis of Sustainability in Real Estate in Job Advertisements and Personal Profiles in Switzerland

The Indian real estate regulations and urban homebuyers: impact assessment

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