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Cited by 105Housing Market and EconomicsAdvanced Neural Network ApplicationsUrban Planning and Valuation

Deep Learning Model for House Price Prediction Using Heterogeneous Data Analysis Along With Joint Self-Attention Mechanism

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Wang, Pei-Ying, Chen, Chiao-Ting, Su, Jain-Wun, Wang, Ting-Yun, Huang, Szu-Hao·2021·IEEE Access
DOI 10.1109/access.2021.3071306
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Abstract

House price prediction is a popular topic, and research teams are increasingly performing related studies by using deep learning or machine learning models. However, because some studies have not considered comprehensive information that affects house prices, prediction results are not always sufficiently precise. Therefore, we propose an end to end joint self-attention model for house prediction. In this model, we import data on public facilities such as parks, schools, and mass rapid transit stations to represent the availability of amenities, and we use satellite maps to analyze the environment surrounding houses. We adopt attention mechanisms, which are widely used in image, speech, and translation tasks, to identify crucial features that are considered by prospective house buyers. The model can automatically assign weights when given transaction data. Our proposed model differs from self-attention models because it considers the interaction between two different features to learn the complicated relationship between features in order to increase prediction precision. We conduct experiments to demonstrate the performance of the model. Experimental data include actual selling prices in real estate transaction data for the period from 2017 to 2018, public facility data acquired from the Taipei and New Taipei governments, and satellite maps crawled using the Google Maps application programming interface. We utilize these datasets to train our proposed and compare its performance with that of other machine learning-based models such as Extreme Gradient Boosting and Light Gradient Boosted Machine, deep learning, and several attention models. The experimental results indicate that the proposed model achieves a low prediction error and outperforms the other models. To the best of our knowledge, we are the first research to incorporate attention mechanism and STN network to conduct house price prediction.

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  • The suggested JGC_MMN method outperforms the current models by a wide margin.

    “the analysis shows that the suggested method outper- forms the current models by a wide margin.”

    From Intelligent Decision Making for Commodities Price Prediction: Opportunities, Challenges and Future Avenues (2025) · cites this paper as Wang et al., 2021

    Section D reports that the paper’s proposed joint self-attention model had better average performance than the benchmarks, and Section F says it outperformed the other models tested. The paper identifies its method as Joint Self-Attention, not JGC_MMN, and these passages do not establish that it won by a wide margin. Page numbers were not available in our copy. (LLM, conf=0.82)

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Cited by

Sorted by citations

Authors
Wang, P.-Y., Chen, C.-T., Su, J.-W., Wang, T.-Y., & Huang, S.-H.
Affiliations
National Yang Ming Chiao Tung University (TW)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021
Volume
9
Pages
55244-55259
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2021.3071306
OpenAlex
W3145114763
Cited by
105
References
3
Citations by year
2026: 6 · 2025: 22 · 2024: 32 · 2023: 18 · 2022: 19 · 2021: 7 · 2020: 1
FWCI
14.51
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Ministry of Science and Technology, Taiwan (MOST 110-2622-8-009-014-TM1, MOST 109-2221-E-009-139, MOST 109-2622-E-009-002-CC2, MOST 109-2218-E-009-015)
  • Financial Technology (FinTech) Innovation Research Center, National Yang Ming Chiao Tung University
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Housing Market and Economics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Urban Planning and Valuation
Keywords
house price prediction, attention mechanism, satellite imagery, housing amenities, heterogeneous data fusion

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