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An Optimal Rubrics-Based Approach to Real Estate Appraisal

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  • Zhangcheng Chen

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Land Information Engineering Technology Research Center, South China Agricultural University, Guangzhou 510642, China)

  • Yueming Hu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Land Information Engineering Technology Research Center, South China Agricultural University, Guangzhou 510642, China)

  • Chen Jason Zhang

    (Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China)

  • Yilun Liu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Land Information Engineering Technology Research Center, South China Agricultural University, Guangzhou 510642, China)

Abstract

Traditional real estate appraisal methods obtain estimates of real estate by using mathematical modeling to analyze the existing sample data. However, the information of sample data sometimes cannot fully reflect the real-time quotes. For example, in a thin real estate market, the correlated sample data for estimated object is lacking, which limits the estimates of these traditional methods. In this paper, an optimal rubrics-based approach to real estate appraisal is proposed, which brings in crowdsourcing. The valuation estimate can serve as a market indication for the potential real estate buyers or sellers. It is not only based on the information of the existing sample data (just like these traditional methods), but also on the extra real-time market information from online crowdsourcing feedback, which makes the estimated result close to that of the market. The proposed method constructs the rubrics model from sample data. Based on this, the cosine similarity function is used to calculate the similarity between each rubric for selecting the optimal rubrics. The selected optimal rubrics and the estimated point are posted on a crowdsourcing platform. After comparing the information of the estimated point with the optimal rubrics on the crowdsourcing platform, those users who are connected with the estimated object complete the appraisal with their knowledge of the real estate market. The experiment results show that the average accuracy of the proposed approach is over 70%; the maximum accuracy is 90%. This supports that the proposed method can easily provide a valuable market reference for the potential real estate buyers or sellers, and is an attempt to use the human-computer interaction in the real estate appraisal field.

Suggested Citation

  • Zhangcheng Chen & Yueming Hu & Chen Jason Zhang & Yilun Liu, 2017. "An Optimal Rubrics-Based Approach to Real Estate Appraisal," Sustainability, MDPI, vol. 9(6), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:6:p:909-:d:99966
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    References listed on IDEAS

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    1. Vincenzo Del Giudice & Pierfrancesco De Paola & Benedetto Manganelli & Fabiana Forte, 2017. "The Monetary Valuation of Environmental Externalities through the Analysis of Real Estate Prices," Sustainability, MDPI, vol. 9(2), pages 1-16, February.
    2. Fengyun Liu & Shuji Matsuno & Reza Malekian & Jin Yu & Zhixiong Li, 2016. "A Vector Auto Regression Model Applied to Real Estate Development Investment: A Statistic Analysis," Sustainability, MDPI, vol. 8(11), pages 1-19, October.
    3. repec:bla:kyklos:v:23:y:1970:i:4:p:775-91 is not listed on IDEAS
    4. John M. Quigley, 1999. "Real Estate Prices and Economic Cycles," International Real Estate Review, Global Social Science Institute, vol. 2(1), pages 1-20.
    5. Case, Bradford & Quigley, John M, 1991. "The Dynamics of Real Estate Prices," The Review of Economics and Statistics, MIT Press, vol. 73(1), pages 50-58, February.
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    Cited by:

    1. Adamczyk Tomasz & Bieda Agnieszka & Parzych Piotr, 2019. "Principles and Criteria for using Statistical Parametric Models and Conditional Models for Valuation of Multi-Component Real Estate," Real Estate Management and Valuation, Sciendo, vol. 27(2), pages 33-43, June.
    2. Renigier-Biłozor, Małgorzata & Źróbek, Sabina & Walacik, Marek & Borst, Richard & Grover, Richard & d’Amato, Maurizio, 2022. "International acceptance of automated modern tools use must-have for sustainable real estate market development," Land Use Policy, Elsevier, vol. 113(C).
    3. Daikun Wang & Victor Jing Li, 2019. "Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    4. Bieda Agnieszka, 2018. "Conditional Model of Real Estate Valuation for Land Located in Different Land Use Zones," Real Estate Management and Valuation, Sciendo, vol. 26(1), pages 122-130, March.

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