AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Big Data Mining and Analytics Article
PDF (8.8 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

House Price Prediction: A Multi-Source Data Fusion Perspective

Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong 999077, China
School of Economics and Management, Beihang University, Beijing 100191, China
Show Author Information

Abstract

House price prediction is of utmost importance in forecasting residential property prices, particularly as the demand for high-quality housing continues to rise. Accurate predictions have implications for real estate investors, financial institutions, urban planners, and policymakers. However, accurately predicting house prices is challenging due to the complex interplay of various influencing factors. Previous studies have primarily focused on basic property information, leaving room for further exploration of more intricate features, such as amenities, traffic, and social sentiments in the surrounding environment. In this paper, we propose a novel approach to house price prediction from a multi-source data fusion perspective. Our methodology involves analyzing house characteristics and incorporating factors from diverse aspects, including amenities, traffic, and emotions. We validate our approach using a dataset of 28550 real-world transactions in Beijing, China, providing a comprehensive analysis of the drivers influencing house prices. By adopting a multi-source data fusion perspective and considering a wide range of influential factors, our approach offers valuable insights into house price prediction. The findings from this study possess the capability to improve the accuracy and effectiveness of house price prediction models, benefiting stakeholders in the real estate market.

Keywords

price predictionreal estatedata miningdata fusionmachine learning

References

[1]

E. Pagourtzi, V. Assimakopoulos, T. Hatzichristos, and N. French, Real estate appraisal: A review of valuation methods, J. Prop. Investment Finance, vol. 21, no. 4, pp. 383–401, 2003.
Crossref Google Scholar
[2]
Y. Zhao, R. Ravi, S. Shi, Z. Wang, E. Y. Lam, and J. Zhao, PATE: Property, amenities, traffic and emotions coming together for real estate price prediction, in Proc. IEEE 9 th Int. Conf. Data Science and Advanced Analytics, Shenzhen, China, 2022, pp. 1–10.
Crossref
[3]

G. Z. Fan, S. E. Ong, and H. C. Koh, Determinants of house price: A decision tree approach, Urban Studies, vol. 43, no. 12, pp. 2301–2315, 2006.
Crossref Google Scholar
[4]

H. Kuşan, O. Aytekin, and İ. Özdemir, The use of fuzzy logic in predicting house selling price, Expert Syst. Appl., vol. 37, no. 3, pp. 1808–1813, 2010.
Crossref Google Scholar
[5]
A. Król, Application of hedonic methods in modelling real estate prices in Poland, in Data Science, Learning by Latent Structures, and Knowledge Discovery, B. Lausen, S. Krolak-Schwerdt, and M. Böhmer, eds. Berlin Germany: Springer, 2015, pp. 501–511.
Crossref
[6]

R. Yayar and D. Demir, Hedonic estimation of housing market prices in Turkey, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 43, pp. 67–82, 2014.
Crossref Google Scholar
[7]

H. Selim, Determinants of house prices in Turkey: Hedonic regression versus artificial neural network, Expert Syst. Appl., vol. 36, no. 2, pp. 2843–2852, 2009.
Crossref Google Scholar
[8]

M. Kryvobokov and M. Wilhelmsson, Analysing location attributes with a hedonic model for apartment prices in Donetsk, Ukraine, Int. J. Strategic Prop. Manage., vol. 11, no. 3, pp. 157–178, 2007.
Crossref Google Scholar
[9]

J. R. Ottensmann, S. Payton, and J. Man, Urban location and housing prices within a hedonic model, J. Reg. Anal. Policy, vol. 38, no. 1, pp. 19–35, 2008.
Google Scholar
[10]

A. Y. Ozalp and H. Akinci, Correction to: The use of hedonic pricing method to determine the parameters affecting residential real estate prices, Arab. J. Geosci., vol. 11, no. 1, p. 2, 2018.
Crossref Google Scholar
[11]

J. Gu, M. Zhu, and L. Jiang, Housing price forecasting based on genetic algorithm and support vector machine, Expert Syst. Appl., vol. 38, no. 4, pp. 3383–3386, 2011.
Crossref Google Scholar
[12]

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, Support vector machines, IEEE Intell. Syst. Their Appl., vol. 13, no. 4, pp. 18–28, 1998.
Crossref Google Scholar
[13]

X. Wang, J. Wen, Y. Zhang, and Y. Wang, Real estate price forecasting based on SVM optimized by PSO, Optik, vol. 125, no. 3, pp. 1439–1443, 2014.
Crossref Google Scholar
[14]

B. Park and J. K. Bae, Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data, Expert Syst. Appl., vol. 42, no. 6, pp. 2928–2934, 2015.
Crossref Google Scholar
[15]

S. Bourassa, E. Cantoni, and M. Hoesli, Predicting house prices with spatial dependence: A comparison of alternative methods, J. Real Estate Res., vol. 32, no. 2, pp. 139–160, 2010.
Crossref Google Scholar
[16]

B. Case, J. Clapp, R. Dubin, and M. Rodriguez, Modeling spatial and temporal house price patterns: A comparison of four models, J. Real Estate Finance Econ., vol. 29, no. 2, pp. 167–191, 2004.
Crossref Google Scholar
[17]

I. H. Gerek, House selling price assessment using two different adaptive neuro-fuzzy techniques, Autom. Constr., vol. 41, pp. 33–39, 2014.
Crossref Google Scholar
[18]

J. M. Montero, R. Mínguez, and G. Fernández-Avilés, Housing price prediction: Parametric versus semi-parametric spatial hedonic models, J. Geogr. Syst., vol. 20, no. 1, pp. 27–55, 2018.
Crossref Google Scholar
[19]
Y. Fu, H. Xiong, Y. Ge, Z. Yao, Y. Zheng, and Z. H. Zhou, Exploiting geographic dependencies for real estate appraisal: A mutual perspective of ranking and clustering, in Proc. 20 th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, New York, NY, USA, 2014, pp. 1047–1056.
Crossref
[20]
Y. Fu, Y. Ge, Y. Zheng, Z. Yao, Y. Liu, H. Xiong, and J. Yuan, Sparse real estate ranking with online user reviews and offline moving behaviors, in Proc. 2014 IEEE Int. Conf. Data Mining, Shenzhen, China, 2014, pp. 120–129.
Crossref
[21]
Walking the walk: How walkability raises home values in U.S. cities, Report, National Association of City Transportation Officials, USA, https://policycommons.net/artifacts/4498224/walking-the-walk/5300886/, 2023.
[22]
E. Washington and E. Dourado, The premium for walkable development under land use regulations, SSRN Electron. J.
[23]
W. Wang, S. Yang, Z. He, M. Wang, J. Zhang, and W. Zhang, Urban perception of commercial activeness from satellite images and streetscapes, in Proc. Web Conf. 2018, Lyon, France, 2018, pp. 647–654.
Crossref
[24]

D. Hristova, L. M. Aiello, and D. Quercia, The new urban success: How culture pays, Front. Phys., vol. 6, p. 27, 2018.
Crossref Google Scholar
[25]
M. de Nadai and B. Lepri, The economic value of neighborhoods: Predicting real estate prices from the urban environment, in Proc. IEEE 5 th Int. Conf. Data Science and Advanced Analytics, Turin, Italy, 2018, pp. 323–330.
Crossref
[26]

M. Kuntz and M. Helbich, Geostatistical mapping of real estate prices: An empirical comparison of kriging and cokriging, Int. J. Geogr. Inf. Sci., vol. 28, no. 9, pp. 1904–1921, 2014.
Crossref Google Scholar
[27]

A. S. Adair, J. N. Berry, and W. S. McGreal, Hedonic modelling, housing submarkets and residential valuation, J. Prop. Res., vol. 13, no. 1, pp. 67–83, 1996.
Crossref Google Scholar
[28]

G. Gao, Z. Bao, J. Cao, A. K. Qin, and T. Sellis, Location-centered house price prediction: A multi-task learning approach, ACM Trans. Intell. Syst. Technol., vol. 13, no. 2, p. 32, 2022.
Crossref Google Scholar
[29]

A. Can, Specification and estimation of hedonic housing price models, Reg. Sci. Urban Econ., vol. 22, no. 3, pp. 453–474, 1992.
Crossref Google Scholar
[30]
D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis. 6th ed. Hoboken, NJ, USA: John Wiley & Sons, 2021.
[31]
T. Chen and C. Guestrin, XGBoost: A scalable tree boosting system, in Proc. 22 nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 785–794.
Crossref
[32]

A. Liaw and M. Wiener, Classification and regression by randomForest, R News, vol. 2, no. 3, pp. 18–22, 2002.
Google Scholar
[33]

M. Riedmiller, Advanced supervised learning in multi-layer perceptrons—From backpropagation to adaptive learning algorithms, Comput. Stand. Interfaces, vol. 16, no. 3, pp. 265–278, 1994.
Crossref Google Scholar
[34]
Y. Zhao, S. Shi, R. Ravi, Z. Wang, E. Y. Lam, and J. Zhao, H4M: Heterogeneous, multi-source, multi-modal, multi-view and multi-distributional dataset for socioeconomic analytics in the case of Beijing, in Proc. IEEE 9 th Int. Conf. Data Science and Advanced Analytics, Shenzhen, China, 2022, pp. 1–10.
Crossref
[35]
B. Zhou, J. Liu, S. Cui, and Y. Zhao, Large-scale traffic congestion prediction based on multimodal fusion and representation mapping, in Proc. IEEE 9 th Int. Conf. Data Science and Advanced Analytics, Shenzhen, China, 2022, pp. 1–9.
Crossref
[36]
B. Zhou, J. Liu, S. Cui, and Y. Zhao, A large-scale spatio-temporal multimodal fusion framework for traffic prediction, Big Data Mining and Analytics.
[37]

R. Fan, J. Zhao, Y. Chen, and K. Xu, Anger is more influential than joy: Sentiment correlation in weibo, PLoS One, vol. 9, no. 10, p. e110184, 2014.
Crossref Google Scholar
[38]
I. Cohen, Y. Huang, J. Chen, and J. Benesty, Pearson correlation coefficient, in Noise Reduction in Speech Processing. Berlin, Germany: Springer, 2009, pp. 1–4.
Crossref
[39]
C. H. R. Madhuri, G. Anuradha, and M. V. Pujitha, House price prediction using regression techniques: A comparative study, in Proc. Int. Conf. Smart Structures and Systems, Chennai, India, 2019, pp. 1–5.
Crossref
[40]
J. C. Pinheiro and D. M. Bates, Linear mixed-effects models: Basic concepts and examples, in Mixed-Effects Models in Sand S-Plus, J. C. Pinheiro and D. M. Bates, eds. New York, NY, USA: Springer, 2000, pp. 3–56.
Crossref
[41]
Y. Zhao, Z. Wang, and E. Y. Lam, Improving source localization by perturbing graph diffusion, in Proc. IEEE 9 th Int. Conf. Data Science and Advanced Analytics, Shenzhen, China, 2022, pp. 1–9.
Crossref
[42]
Y. Zhao and E. Y. Lam, SASA: Saliency-aware self-adaptive snapshot compressive imaging, In Proc. 2024 IEEE Int. Conf. Acoustics, Speech and Signal Processing, Seoul, Korea, Republic of, 2024, pp. 2370–2374.
Crossref
[43]
Y. Zhao, S. Zheng, and X. Yuan, Deep equilibrium models for snapshot compressive imaging, in Proc. 37 th AAAI Conf. Artificial Intelligence, Washington, DC, USA, 2023, pp. 406.
Crossref
[44]
Y. Zhao, G. Li, and E. Y. Lam, Cross-camera human motion transfer by time series analysis, in Proc. 2024 IEEE Int. Conf. Acoustics, Speech and Signal Processing, Seoul, Korea, Republic of, 2024, pp. 4985–4989.
Crossref
[45]
Y. Zhao, H. Zheng, Z. Wang, J. Luo, and E. Y. Lam, Manet: Improving video denoising with a multi-alignment network, in Proc. 2022 IEEE Int. Conf. Image Processing, Bordeaux, France, 2022, pp. 2036–2040.
Crossref
Big Data Mining and Analytics
Volume 7 Issue 3,
September 2024
Pages 603-620
DOI: 10.26599/BDMA.2024.9020019
Cite this article:
Zhao Y, Zhao J, Lam EY. House Price Prediction: A Multi-Source Data Fusion Perspective. Big Data Mining and Analytics, 2024, 7(3): 603-620. https://doi.org/10.26599/BDMA.2024.9020019

451

Views

125

Downloads

2

Crossref

1

Web of Science

3

Scopus

0

CSCD

Altmetrics
Received: 24 November 2023
Revised: 12 March 2024
Accepted: 22 March 2024
Published: 28 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return