A bibliometric analysis of property valuation research

Binoy, B V; Naseer, M.A.; Kumar, Prem; Lazar, Nina

doi:10.1108/ijhma-09-2020-0115

Cited by 17 publications

(5 citation statements)

References 44 publications

(57 reference statements)

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“…For instance, Krause and Bitter (2012) examined the direction of valuation research in terms of spatial econometrics, land values and sustainability. At the same time, Binoy et al (2022) conducted a bibliometric analysis on the trend of hedonic pricing model (HPM) studies in property valuation. No study has focused on a bibliometric or trend analysis, scoping review or systematic review of client influence on property valuation.…”

Section: Literature Reviewmentioning

confidence: 99%

Client influence in property valuation: a scoping literature review

Adilieme

Abidoye

Lee

2023

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PurposeThe property valuation process involves the property valuer expressing expertise in reaction to a client' instruction. However, there are instances where clients, driven by self-interest, impose their will to influence valuers into returning property valuation figures that are not the true reflection of the valuer's assessment. This paper set out to revisit the issue of client influence in property valuation by conducting a scoping review to establish key findings, gaps, implications and solutions.Design/methodology/approachIn total, 21 articles on client influence published from 1997 till date were systematically obtained from Scopus, Web of Science, Google Scholar and through citation searching and reviewed through a “Patterns, Advances, Gaps, Evidence for practice and Research recommendations (PAGER)” framework. Further analysis and visualisation were performed using VOSviewer software.FindingsThis study found that based on the number of studies, the issue of client influence has received empirical attention, which is few and far between, with financial institutions identified as the major culprits in most of those studies. One core reason for this is the stakes involved in the finance sector, which relies on property valuation for loan disbursement and performance measurement. Furthermore, previous studies have focused on identifying the issue through the lens of the property valuer without giving recourse to the client's perspective on what may drive the issue.Research limitations/implicationsThis study provides evidence that the issue of client influence persists, with some elements of bias in the methodology. Furthermore, the solutions proffered have focused on the valuer and have not been tested to ascertain their effectiveness. Future studies can consider examining the issue from the perspective of financial institutions.Originality/valueThis study is one of the first review studies on client influence on property valuation. It is also the first to identify a pattern in client influence studies that points to the issue being perpetuated by financial institutions. Furthermore, it is the first in recent time to reveal how limited study has been conducted in the area as well as the solutions which have neither been tested nor implemented.

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Section: Literature Reviewmentioning

confidence: 99%

Client influence in property valuation: a scoping literature review

Adilieme

Abidoye

Lee

2023

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“…Yu et al (2019) analyzed and presented an intellectual structure for the Journal of Civil Engineering and Management between 2008 and 2018. Bibliometric studies have also been conducted for real estate journals and topics (Binoy et al , 2022; Liu et al , 2021; Nguyen et al , 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

A bibliometric analysis of the first 20 years of the Journal of Corporate Real Estate

Maggon

2022

JCRE

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Purpose Journal of Corporate Real Estate (JCRE) is an international journal of repute. JCRE is an interdisciplinary journal focusing on corporate real estate management and caters to the needs of real estate owners and end-users. The journal's scope includes private and public sector corporate real estate owned and used by businesses to run their operations. This paper aims to analyze the journal's impact using bibliometric analysis from 1998 to 2017 on 380 publications. Design/methodology/approach The network analysis is performed using VOSviewer software and Biblioshiny R studio. The mapping presents citation structures, coauthorship patterns, bibliographic coupling and other network analyses. Findings The results show that the journal is growing in terms of citations and impact. The findings document various mapping criteria and their summaries. Seven major clusters are identified to delineate the underlying themes of publications in JCRE. Originality/value To the best of the authors’ knowledge, this paper offers the original and first bibliometric analysis of the journal’s impact over the first two decades. The findings are relevant for researchers and real estate stakeholders.

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“…In the market, real estate with various characteristics is traded at non-uniform time intervals, so it is difficult to scale it when applying time series analysis. For a study on various valuation models, please refer to Pagourtzi et al (2003), Gabrielli and French (2021), Binoy et al (2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Combination of Machine Learning-Based Automatic Valuation Models for Residential Properties in South Korea

Hong

Kim

2022

International Journal of Strategic Property Management

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The applicability of machine learning (ML) techniques has recently been expanding to include automatic real estate valuation models. The main advantage of this technique is that it can better capture complexity in the value determination process. Therefore, the performance of these techniques is shown to be superior to conventional models. In this paper, the latest ML algorithms (i.e., support vector machine, random forest, XGBoost, LightGBM, and CatBoost algorithms) are examined as automatic valuation models, and several combination methods are proposed to improve the models’ predictive power. We applied ML models to approximately 57,000 records on apartment transactions, which were provided by South Korea’s Ministry of Land, Infrastructure, and Transport, that occurred in Seoul in 2018. The results are as follows. First, ML-based predictors (especially, the latest decision tree-based algorithms) are more performative than conventional models. Second, the prediction error from a model can be partially offset by another model’s error, which implies that an efficient averaging of the predictors improves their predictive accuracy. Third, the models’ relative performance may be relearned by the ML algorithms, which means that they can also be used to recommend which algorithm should be selected for making predictions.

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A bibliometric analysis of property valuation research

Cited by 17 publications

References 44 publications

Client influence in property valuation: a scoping literature review

Client influence in property valuation: a scoping literature review

A bibliometric analysis of the first 20 years of the Journal of Corporate Real Estate

Combination of Machine Learning-Based Automatic Valuation Models for Residential Properties in South Korea

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