Machine Learning Based Perspectives for Housing Market Crash Prediction
Year: 2025
Type: article
Abstract: The recession of 2008, commonly referred to as the "Subprime Mortgage Crisis," was the worst to hit the USA and had an impact on the entire world, costing more than 8.4 million people their employment alone in the USA. From late 2006 to the end of 2009, the crises repeatedly forced innocent people to leave their homes, and the Gross Domestic Product (GDP) dropped as low as 4.3%. In order to assist in estimating future market values and prices so that a real estate market recession can be averted, this working paper analyses old and present prices, levels, and interest rates to determine at what rate the market could have been rescued from falling. A house market crash prediction with machine learning techniques including Linear Regression, Hidden Markov Model, and Long Short Term Memory is presented here.
Author: Journal of Theoretical and Applied Information Technology
Language: English
Topic: Housing Market and Economics
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7083588240
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