International Journal of Advanced Culture Technology

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
권호 표지
DOI QR코드

DOI QR Code

Prediction Model of Real Estate Transaction Price with the LSTM Model based on AI and Bigdata

  • Received : 2022.02.15
  • Accepted : 2022.03.08
  • Published : 2022.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

Keywords

References

  1. Karevan, Z., and Suykens, J. A., "Transductive LSTM for Time-series Prediction: An Application to Weather Forecasting," Neural Networks, Vol. 125, 2020, pp.1-9. DOI: https://doi.org/10.1016/j.neunet.2019.12.030
  2. Baek, Y., and Kim, H. Y., "ModAugNet: A New Forecasting Framework for Stock Market Index Value with An Overfitting Prevention LSTM Module and A Prediction LSTM Module," Expert Systems with Applications, Vol.113, 2018, pp. 457-480. DOI: https://doi.org/10.1016/j.eswa.2018.07.019
  3. Azadeh, A., Ziaei, B., and Moghaddam, M., "A Hybrid Fuzzy Regression-fuzzy Cognitive Map Algorithm for Forecasting and Optimization of Housing Market Fluctuations," Expert Systems with Applications, Vol. 39, No.1, 2012, pp. 298-315. DOI: https://doi.org/10.1016/j.eswa.2011.07.020
  4. Fan, G. Z., Ong, S. E., and Koh, H. C.,"Determinants of House Price: A Decision Tree Approach," Urban Studies, Vol. 43, No. 12, 2006, pp. 2301-2315. DOI: https://doi.org/10.1080/00420980600990928
  5. Bin, O., "A Prediction Comparison of Housing Sales Prices by Parametric versus Semi-parametric Regressions," Journal of Housing Economics, Vol. 13, No. 1, 2004, pp. 68-84. DOI: https://doi.org/10.1016/j.jhe.2004.01.001
  6. Plakandaras, V., Gupta, R., Gogas, P., and Papadimitriou, T., "Forecasting the US Real House Price Index," Economic Modelling, Vol. 45, 2015, pp. 259-267. DOI: https://doi.org/10.1016/j.econmod.2014.10.050
  7. Wang, X., Wen, J., Zhang, Y., and Wang, Y., "Real Estate Price Forecasting Based on SVM Optimized by PSO," Optik, Vol. 125, No. 3, 2014, pp. 1439-1443. DOI: https://doi.org/10.1016/j.ijleo.2013.09.017
  8. Patrick, J., Okunev, J., Ellis, C., and David, M., "Comparing Univariate Forecasting Techniques in Property Markets," Journal of Real Estate Portfolio Management, Vol. 6, No. 3, 2000, pp. 283-306. DOI: https://doi.org/10.1080/10835547.2000.12089608
  9. Y.I. Kim, S.S. Yang, S.S. Lee, S.C. Park, "Design and Implementation of Mobile CRM Utilizing Bigdata Analysis Techniques", The Journal of The Institute of Internet, Broadcasting and Communication, Vol. 14, No. 6, 2014, pp. 289-294. DOI: https://doi.org/10.7236/JIIBC.2014.14.6.289
  10. S.J. Oh, "Design of a Smart Application using Bigdata", The Journal of The Institute of Internet, Broadcasting and Communication, Vol. 15, No. 6, 2015, pp. 17-24. DOI: https://www.earticle.net/Article/A259710 https://doi.org/10.7236/JIIBC.2015.15.6.17