Towards an intelligent residential appraisal model

Jenkins, David H.; Lewis, Owen M.; Almond, Nigel; Gronow, Stuart; Ware, Andrew

doi:10.1080/095999199368265

Cited by 19 publications

(9 citation statements)

References 3 publications

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“…It is clear that most of the authors were faculty members of universities, while a few real estate practitioners affiliated to research institutes, real estate firms and government organizations contributed to this research topic. This corroborates the findings of Adewunmi and Olaleye (2011) and the position of Jenkins et al (1999) that university scholars are the highest contributors to real estate research compared to professionals, which is also the same in the construction management field of study (see Al-Sharif and Kaka, 2004). However, it is pertinent to note that the first application of this technique in the real estate domain was conducted by an industry practitioner, which suggests that practice and research are vital for the sustainability of any profession (Hemsley-Brown and Sharp, 2003).…”

Section: Ann In Property Valuationsupporting

confidence: 84%

Artificial neural network in property valuation: application framework and research trend

Abidoye

Chan

2017

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Purpose The predictive accuracy and reliability of artificial intelligence models, such as the artificial neural network (ANN), has led to its application in property valuation studies. However, a large percentage of such previous studies have focused on the property markets in developed economies, and at the same time, effort has not been put into documenting its research trend in the real estate domain. The purpose of this paper is to critically review the studies that adopted ANN for property valuation in order to present an application guide for researchers and practitioners, and also establish the trend in this research area. Design/methodology/approach Relevant articles were retrieved from online databases and search engines and were systematically analyzed. First, the background, the construction and the strengths and weaknesses of the technique were highlighted. In addition, the trend in this research area was established in terms of the country of origin of the articles, the year of publication, the affiliations of the authors, the sample size of the data, the number of the variables used to develop the models, the training and testing ratio, the model architecture and the software used to develop the models. Findings The analysis of the retrieved articles shows that the first study that applied ANN in property valuation was published in 1991. Thereafter, the technique received more attention from 2000. While a quarter of the articles reviewed emanated from the USA, the rest were conducted in mostly developed countries. Most of the studies were conducted by universities scholars, while very few industry practitioners participated in the research works. Also, the predictive accuracy of the ANN technique was reported in most of the papers reviewed, but a few reported otherwise. Research limitations/implications The articles that are not indexed in the search engines and databases searched and also not available in the public domain might not have been captured in this study. Practical implications The findings of this study reveal a gap between the valuation practice in developed and developing property markets and also the contributions of real estate practitioners and universities scholars to real estate research. A paradigm shift in the valuation practice in developing nations could lead to achieving a sustainable international valuation practice. Originality/value This paper presents the trend in this research area that could be useful to real estate researchers and practitioners in different property markets around the world. The findings of this study could also encourage collaboration between industry professionals and researchers domiciled in both developed and developing countries.

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Section: Ann In Property Valuationsupporting

confidence: 84%

Artificial neural network in property valuation: application framework and research trend

Abidoye

Chan

2017

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“…One of the input variables may of course be the price variable; if this (or any other) layer has a special interest for us, it will be selected for closer scrutiny. For further information on the SOM as a method for clustering and visualisation of data see Deboeck & Kohonen (1998); for applications in related problem areas, see Hatzichristos (2004) in population geography; and Jenkins et al (1999) in property valuation.…”

Section: Era Of Constructionmentioning

confidence: 99%

The Housing Market Dynamics of Two Budapest Neighbourhoods

Kauko

2009

Housing Studies

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This study deals with residential location modelling of Budapest, Hungary, using a neural network approach known as the self-organising map (SOM). After identifying various urban elements, the analysis focuses on the dynamics of two selected inner-city neighbourhoods: the middle parts of the districts VIII (Józsefváros) and IX (Ferencváros) respectively. These adjacent, but different, areas in the south-eastern part of the inner city have both received attention as subjects for substantial rehabilitation in recent decades. When the differentiation of micro-locations in the targeted areas was modelled using the SOM over a period of six yearly cross-sections, notable dynamics could be identified in terms of both actual changes in the fundamental quality, and expected house price escalations that are not related to fundamentals. In particular, the evidence reveals how housing market development is related to most localised processes of social and physical upgrading occurring in an urban setting.

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“…Aside from doubting the objectivity of appraisals and questioning the underlying institutional settings, researchers are dissatisfied with the current situation in which the comparable sales approach remains central to the practice of appraising residential real estate (Jenkins et al, 1999), when in fact many different valuation methods can be used (Pagourtzi, Assimakopoulos, Hatzichristos, and French, 2003;Lins, Novaes, and Legey, 2005).…”

Section: Research On Appraisals and Appraisersmentioning

confidence: 99%

Under What Circumstances do First-time Homebuyers Overpay? — An Empirical Analysis Using Mortgage and Appraisal Data

Shui

Murthy

2019

Journal of Real Estate Research

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We study whether first-time homebuyers overpay for their homes and whether the magnitude of overpayment varies with the diligence of appraisers involved. We present a robust result that first-time homebuyers sort into smaller and cheaper houses, but that once observed and unobserved house characteristics are controlled for, they pay a premium compared to their more experienced counterparts. Our analysis additionally suggests that certain appraisals and appraisers might be able to mitigate this overpayment by inducing downward renegotiation. This research is among the first to contribute both theoretically and empirically to the literature on first-time homebuyers' sales transactions.

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Towards an intelligent residential appraisal model

Cited by 19 publications

References 3 publications

Artificial neural network in property valuation: application framework and research trend

Artificial neural network in property valuation: application framework and research trend

The Housing Market Dynamics of Two Budapest Neighbourhoods

Under What Circumstances do First-time Homebuyers Overpay? — An Empirical Analysis Using Mortgage and Appraisal Data

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