An integrated cost-based approach for real estate appraisals

Guo, Jingjuan; Xu, Shukai; Bi, Zhuming

doi:10.1007/s10799-012-0152-7

Cited by 16 publications

(14 citation statements)

References 26 publications

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“…Real Estate Appraisal. Traditional real estate appraisal methodologies can be mainly categorized into three classes, i.e., sales comparison based approaches [25], cost based approaches [12], and income based approaches [2]. The sales comparison approach estimates the market value of real estate depending on some forms of comparison.…”

Section: Related Workmentioning

confidence: 99%

“…Prior studies on real estate appraisal can be categorized into two classes: (1) Empirical Appraisal Methods (EAMs), such as sales comparison approach [25], cost approach [12], and income approach [2], either heavily depend on the accuracy, availability, and timeliness of sale transaction data, or require strong domain knowledge to perform [26]; (2) Automated Valuation Methods (AVMs) estimate the market value of a property based on automatic machine learning techniques, such as linear regression [1,34], support vector regression [22], boosted regression trees [11,27] and artificial neural networks [17,30,31]. Compared with EAMs, AVMs are easy-to-use even for non-domain experts and are widely used in practice.…”

Section: Introductionmentioning

confidence: 99%

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MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

Zhang

Líu

Zha

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e.g., real estate agents, appraisers, lenders, and buyers). However, it is a nontrivial task for accurate real estate appraisal because of three major challenges: (1) The complicated influencing factors for property value; (2) The asynchronously spatiotemporal dependencies among real estate transactions; (3) The diversified correlations between residential communities. To this end, we propose a Multi-Task Hierarchical Graph Representation Learning (MugRep) framework for accurate real estate appraisal. Specifically, by acquiring and integrating multi-source urban data, we first construct a rich feature set to comprehensively profile the real estate from multiple perspectives (e.g., geographical distribution, human mobility distribution, and resident demographics distribution). Then, an evolving real estate transaction graph and a corresponding event graph convolution module are proposed to incorporate asynchronously spatiotemporal dependencies among real estate transactions. Moreover, to further incorporate valuable knowledge from the view of residential communities, we devise a hierarchical heterogeneous community graph convolution module to capture diversified correlations between residential communities. Finally, an urban district partitioned multi-task learning module is introduced to generate differently distributed value opinions for real estate. Extensive experiments on two real-world datasets demonstrate the effectiveness of MugRep and its components and features. CCS CONCEPTS• Information systems → Data mining; Spatial-temporal systems; • Applied computing → Marketing.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

Zhang

Líu

Zha

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

show abstract

“…Glennon et al (2018) consider five different forecast combination methods to construct a weighted average of component forecasts [104]. Guo et al (2014) integrate some elements from a sales comparison approach and income approach into the cost approach to improve the accuracy of the valuation of real estate [105]. The second type is like the models in Sections 4.1 and 4.2.…”

Section: Mix-based Modelmentioning

confidence: 99%

Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review

Wang

2019

Sustainability

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With the increasing volume and active transaction of real estate properties, mass appraisal has been widely adopted in many countries for different purposes, including assessment of property tax. In this paper, 104 papers are selected for the systematic literature review of mass appraisal models and methods from 2000 to 2018. The review focuses on the application trend and classification of mass appraisal and highlights a 3I-trend, namely AI-Based model, GIS-Based model and MIX-Based model. The characteristics of different mass appraisal models are analyzed and compared. Finally, the future trend of mass appraisal based on model perspective is defined as “mass appraisal 2.0”: mass appraisal is the appraisal procedure of model establishment, analysis and test of group of properties as of a given date, combined with artificial intelligence, geo-information systems, and mixed methods, to better model the real estate value of non-spatial and spatial data.

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“…Real estate assets "are an integral part of an overall economy", and as such "changes in real estate value or transaction volume may have consequences in almost every sector of the economy" (Maier & Herath, 2009, p. 2). As a result, the valuation of real estate investments is crucial, and of significant interest to a variety of participants, namely: (1) the sellers and buyers of properties, who need to know the total value of their personal assets; (2) governments and municipalities, whose revenues depend in part on real estate taxes; (3) financial institutions, who aim to implement banking policies with minimized risks; and (4) brokerage firms, who need correct real estate evaluations in order to help their clients make decisions (Guo, Xu, & Bi, 2014). In this sense, the real estate market is characterized by investments which hold "the high-risk and high return of economic activity" (Minli & Wenpo, 2012, p. 1815; and several methodological studies have been developed with a view to more accurate assessments of investment risk in the sector.…”

Section: Literature Reviewmentioning

confidence: 99%

Barriers to Real Estate Investments for Residential Rental Purposes: Mapping Out the Problem

Pires

Ferreira

Jalali

et al. 2018

International Journal of Strategic Property Management

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Abstract. The recent economic crisis led to significant changes in the real estate market; one of which was a shift toward home rental (rather than buying). Real estate investors have an important role in the growth of the rental market. However, there are often hindrances to investing for residential rental purposes. In order to overcome these barriers, they first need to be identified and understood. With this in mind, the main focus of this investigation was the creation of a conceptual model, through fuzzy cognitive mapping, to identify and understand the cause-and-effect relationships between the factors that represent an obstacle to real estate investments for residential rental purposes. The results show that cognitive maps can be of great use for the structuring of complex decision problems, minimizing the number of factors left out of the decision-making process. In particular, the tenant risk behavior, property location and associated costs (for the owner) were identified as the main obstacles to real estate investment. The practical implications of the model, as well as the advantages and limitations of the process followed, are also discussed.

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An integrated cost-based approach for real estate appraisals

Cited by 16 publications

References 26 publications

MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review

Barriers to Real Estate Investments for Residential Rental Purposes: Mapping Out the Problem

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