TY - GEN
T1 - Deep Learning for Real Estate Trading
AU - Zhao, Yun
AU - Chetty, Girija
AU - Tran, Dat
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Artificial intelligence (AI) and Machine Learning techniques have been making impact on the real estate industry recently. Increasingly, several real estate companies have started to use a variety of AI techniques to optimize their property business. Ma-chine learning (ML) technology for providing support on real es-tate investment decisions, allows investigation of historical property sales data by computer algorithms to automatically predict house prices. Real estate professionals can leverage sophisticate ML techniques to analyse sales data as benchmarks and make appraisals for their home selling clients and customers. ML technologies not only make predictions or classifications, but also can assistant real estate professionals for investment purposes by providing a trading strategy. In this paper, we propose a novel machine learning model, based on a standard deep reinforcement learning (DRL) model, enhanced with a combination of two popular time series algorithms, the Gramian Angular Field (GAF) and long short-term memory (LSTM) algorithms for providing decision support on real estate trading strategy. Our goal is to explore if the proposed enhanced DRL model can make profitable trading strategy for a long-time investment, such as the real estate markets.
AB - Artificial intelligence (AI) and Machine Learning techniques have been making impact on the real estate industry recently. Increasingly, several real estate companies have started to use a variety of AI techniques to optimize their property business. Ma-chine learning (ML) technology for providing support on real es-tate investment decisions, allows investigation of historical property sales data by computer algorithms to automatically predict house prices. Real estate professionals can leverage sophisticate ML techniques to analyse sales data as benchmarks and make appraisals for their home selling clients and customers. ML technologies not only make predictions or classifications, but also can assistant real estate professionals for investment purposes by providing a trading strategy. In this paper, we propose a novel machine learning model, based on a standard deep reinforcement learning (DRL) model, enhanced with a combination of two popular time series algorithms, the Gramian Angular Field (GAF) and long short-term memory (LSTM) algorithms for providing decision support on real estate trading strategy. Our goal is to explore if the proposed enhanced DRL model can make profitable trading strategy for a long-time investment, such as the real estate markets.
KW - Artificial Intelligence
KW - Deep reinforcement learning
KW - Gramian Angular Field
KW - Machine Learning
KW - Real Estate Trading
UR - http://www.scopus.com/inward/record.url?scp=85153681912&partnerID=8YFLogxK
U2 - 10.1109/CSDE56538.2022.10089222
DO - 10.1109/CSDE56538.2022.10089222
M3 - Conference contribution
AN - SCOPUS:85153681912
T3 - Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022
BT - Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022
PB - IEEE, Institute of Electrical and Electronics Engineers
T2 - 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022
Y2 - 18 December 2022 through 20 December 2022
ER -