Deep Learning for Real Estate Trading

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)9781665453059
DOIs
Publication statusPublished - 2022
Event2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022 - Gold Coast, Australia
Duration: 18 Dec 202220 Dec 2022

Publication series

NameProceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022

Conference

Conference2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022
Country/TerritoryAustralia
CityGold Coast
Period18/12/2220/12/22

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