Skip to main content
Log in

Research on real estate pricing methods based on data mining and machine learning

  • S.I. : SPIoT 2020
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper aims to study the actual utility of real estate pricing models based on data mining and machine learning. In order to achieve this goal, this paper introduces appropriate trend estimation methods, adjusts pricing models and processes, and realizes trend estimation that changes over time to make the resulting pricing model have advantages such as dynamics, accuracy, and flexibility over the original model. Moreover, this paper proposes a real estate pricing model based on the quadratic exponential smoothing time-varying trend estimation. In addition, this paper uses the quadratic exponential smoothing method to calculate the index trend in the random process of the house price index, and performs segmentation processing on the pricing model according to the index cycle, so as to obtain the real estate pricing method under the time-varying trend. Finally, this paper adjusts the volatility parameters and the mean recovery rate to time-varying piecewise functions, uses the quadratic variation to calculate the volatility parameters and the martingale valuation method to calculate the mean recovery rate parameters, and establishes the real estate pricing model of the time-varying O–U process. Case studies show that the model constructed in this paper has good performance and has certain practical effects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Sarip AG, Hafez MB, Daud MN (2016) Application of fuzzy regression model for real estate price prediction[J]. Malaysian Journal of Computer Science 29(1):15–27

    Article  Google Scholar 

  2. Park B, Bae JK (2015) Using machine learning algorithms for housing price prediction: the case of Fairfax County, Virginia housing data. Expert Syst Appl 42(6):2928–2934

    Article  Google Scholar 

  3. Deng Y, Quigley JM (2008) Index revision, house price risk, and the market for house price derivatives. J Real Estate Finance Econ 37(3):191–209

    Article  Google Scholar 

  4. Wang X, Wen J, Zhang Y et al (2014) Real estate price forecasting based on SVM optimized by PSO. Optik 125(3):1439–1443

    Article  Google Scholar 

  5. Jiang L, Phillips PCB, Yu J (2015) New methodology for constructing real estate price indices applied to the Singapore residential market. J Bank Finance 61:S121–S131

    Article  Google Scholar 

  6. Kuntz M, Helbich M (2014) Geostatistical mapping of real estate prices: an empirical comparison of kriging and cokriging. Int J Geogr Inf Sci 28(9):1904–1921

    Article  Google Scholar 

  7. Guan J, Shi D, Zurada JM et al (2014) Analyzing massive data sets: an adaptive fuzzy neural approach for prediction, with a real estate illustration. J Org Comput Electron Commer 24(1):94–112

    Article  Google Scholar 

  8. You Q, Pang R, Cao L et al (2017) Image-based appraisal of real estate properties. IEEE Trans Multimed 19(12):2751–2759

    Article  Google Scholar 

  9. Hromada E (2015) Mapping of real estate prices using data mining techniques. Procedia Eng 123:233–240

    Article  Google Scholar 

  10. Chow YL, Hafalir IE, Yavas A (2015) Auction versus negotiated sale: evidence from real estate sales. Real Estate Econ 43(2):432–470

    Article  Google Scholar 

  11. Cellmer R (2014) The possibilities and limitations of geostatistical methods in real estate market analyses. Real Estate Manag Valuat 22(3):54–62

    Article  Google Scholar 

  12. Poursaeed O, Matera T, Belongie S (2018) Vision-based real estate price estimation. Mach Vis Appl 29(4):667–676

    Article  Google Scholar 

  13. Kurlat P, Stroebel J (2015) Testing for information asymmetries in real estate markets. Rev Financ Stud 28(8):2429–2461

    Article  Google Scholar 

  14. Nagaraja C, Brown L, Wachter S (2014) Repeat sales house price index methodology. J Real Estate Lit 22(1):23–46

    Article  Google Scholar 

  15. Ballings M, Van den Poel D, Hespeels N et al (2015) Evaluating multiple classifiers for stock price direction prediction. Expert Syst Appl 42(20):7046–7056

    Article  Google Scholar 

  16. Cvijanović D (2014) Real estate prices and firm capital structure. Rev Financ Stud 27(9):2690–2735

    Article  Google Scholar 

  17. Nowak A, Smith P (2017) Textual analysis in real estate. J Appl Econom 32(4):896–918

    Article  MathSciNet  Google Scholar 

  18. Dettling LJ, Kearney MS (2014) House prices and birth rates: the impact of the real estate market on the decision to have a baby. J Public Econ 110:82–100

    Article  Google Scholar 

  19. Hosaka T (2019) Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Syst Appl 117:287–299

    Article  Google Scholar 

  20. Kim Y, Choi S, Yi MY (2020) Applying comparable sales method to the automated estimation of real estate prices. Sustainability 12(14):5679

    Article  Google Scholar 

  21. Feng F, He X, Wang X et al (2019) Temporal relational ranking for stock prediction. ACM Trans Inf Syst (TOIS) 37(2):1–30

    Article  Google Scholar 

  22. Iturriaga FJL, Sanz IP (2015) Bankruptcy visualization and prediction using neural networks: a study of US commercial banks. Expert Syst Appl 42(6):2857–2869

    Article  Google Scholar 

  23. Baldauf M, Garlappi L, Yannelis C (2020) Does climate change affect real estate prices? Only if you believe in it. Rev Financ Stud 33(3):1256–1295

    Article  Google Scholar 

  24. Murfin J, Spiegel M (2020) Is the risk of sea level rise capitalized in residential real estate? Rev Financ Stud 33(3):1217–1255

    Article  Google Scholar 

  25. Geltner D (2015) Real estate price indices and price dynamics: an overview from an investments perspective. Annu Rev Financ Econ 7:615–633

    Article  Google Scholar 

  26. Yeh IC, Hsu TK (2018) Building real estate valuation models with comparative approach through case-based reasoning. Appl Soft Comput 65:260–271

    Article  Google Scholar 

  27. García-Magariño I, Medrano C, Delgado J (2020) Estimation of missing prices in real-estate market agent-based simulations with machine learning and dimensionality reduction methods. Neural Comput Appl 32(7):2665–2682

    Article  Google Scholar 

  28. Du D, Li A, Zhang L (2014) Survey on the applications of big data in Chinese real estate enterprise. Procedia Comput Sci 30:24–33

    Article  Google Scholar 

  29. Renigier-Biłozor M, Wisniewski R, Kaklauskas A et al (2014) Rating methodology for real estate markets–Poland case study. Int J Strateg Prop Manag 18(2):198–212

    Article  Google Scholar 

  30. Pivo G (2014) The effect of sustainability features on mortgage default prediction and risk in multifamily rental housing. J Sustain Real Estate 5(1):149–170

    Article  Google Scholar 

  31. Bollerslev T, Patton AJ, Wang W (2016) Daily house price indices: construction, modeling, and longer-run predictions. J Appl Econom 31(6):1005–1025

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by Postdoctoral Research Foundation of China (No. 2019M663405).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jingfu Lu.

Ethics declarations

Conflict of interest

The authors have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, Y., Lu, J., Shen, D. et al. Research on real estate pricing methods based on data mining and machine learning. Neural Comput & Applic 33, 3925–3937 (2021). https://doi.org/10.1007/s00521-020-05469-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05469-3

Keywords

Navigation