Prediction Model of Real Estate ROI with the LSTM Model based on AI and Bigdat

다국어 초록 (Multilingual Abstract)

Across the world, ‘housing’ comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price ‘return rate’ prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government’s data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved.
All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

참고문헌 (Reference)

1 Karevan, Z., "Transductive LSTM for Time-series Prediction : An Application to Weather Forecasting" 125 : 1-9, 2020

2 Wang, X., "Real Estate Price Forecasting Based on SVM Optimized by PSO" 125 (125): 1439-1443, 2014

3 Baek, Y., "ModAugNet : A New Forecasting Framework for Stock Market Index Value with An Overfitting Prevention LSTM Module and A Prediction LSTM Module" 113 : 457-480, 2018

4 Antipov, E. A., "Mass Appraisal of Residential Apartments: An Application of Random Forest for Valuation and A CART-based Approach for Model Diagnostics" 39 (39): 1772-1778, 2012

5 Plakandaras, V., "Forecasting the US Real House Price Index" 45 : 259-267, 2015

6 Patrick, J., "Comparing Univariate Forecasting Techniquesin Property Markets" 6 (6): 283-306, 2000

7 Bin, O., "A Prediction Comparison of Housing Sales Prices by Parametric versus Semi-parametric Regressions" 13 (13): 68-84, 2004

8 Azadeh, A., "A Hybrid Fuzzy Regression-fuzzy Cognitive Map Algorithm for Forecasting and Optimization of Housing Market Fluctuations" 39 (39): 298-315, 2012