From Offer to Close: A Machine Learning Approach to Forecast Real Estate Transaction Outcomes

Yu Zhao

No sxmq2_v1, OSF Preprints from Center for Open Science

Abstract: Accurately forecasting whether a real estate transaction will close is crucial for agents, lenders, and investors, impacting resource allocation, risk management, and client satisfaction. This task, however, is complex due to a combination of economic, procedural, and behavioral factors that influence transaction outcomes. Traditional machine learning approaches, particularly gradient boosting models like Gradient Boost Decision Tree, have proven effective for tabular data, outperforming deep learning models on structured datasets. However, recent advances in attention-based deep learning models present new opportunities to capture temporal dependencies and complex interactions within transaction data, potentially enhancing prediction accuracy. This article explores the challenges of forecasting real estate transaction closures, compares the performance of machine learning models, and examines how attention-based models can improve predictive insights in this critical area of real estate analytics.

Date: 2024-11-08
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:sxmq2_v1

DOI: 10.31219/osf.io/sxmq2_v1

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