Early Warning System of Housing Market Using Machine Learning

Abstract

This study proposes an early warning system for risks of the housing market based on machine learning models. We adopt a signal approach to detect the housing market risk and establish the early warning system using classification methods. Considering the moment when the housing market falls into recession as a warning signal, we set the signal as the price which is more than the sum of the average and standard deviation of upcoming prices. The detected signals are consistent with empirical observations in the Korean housing market. We select the best performing function among classification models for machine learning which predicts a warning signal. As a result of an intercomparison of models including the logistic regression, the support vector machine, the random forest and the artificial neural network with the use of inputs such as housing price indices, macroeconomic variables and other housing market variables, we find that the random forest demonstrates the highest prediction performance. Our early warning system yields policy implications in terms of relevant detection of price fluctuations in the housing market.

Changes in Social Media Big Data on Healing Forests: A Time-Series Analysis on the Use Behavior of Healing Forests before and after the COVID-19 Pandemic in South Korea

“…Words with higher TF-IDF weight values are more likely to determine the topic or meaning of the documents they belong to, and this measure can be used to extract the main keywords [17]. Therefore, we focused on the top 100 occurrences of words by TF-IDF weight by time period.…”

“…(Manufacturer: TheIMC, Daegu Metropolitan City, Republic of Korea) was used for data collection, refinement, and matrix generation. Textom is a big data processing solution that automatically collects data from various Internet channels by channel and processes it in batches, including purification and matrix production [15], and is actively used for text mining analysis in Korea [14,16,17]. We used Keyword Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) analysis, Convergence of iterated CORrelation (CONCOR) analysis, and Quadratic Assignment Procedure (QAP) correlation analysis with the matrix generated by text mining analysis technique using Textom to identify changes in usage behavior by period.…”