SUMMARY ․ 203
SUMMARY
Housing Market Analysis Reflecting Factors Influencing Home Buying Decision Making
Park Chungyu, Kim Jeehye, Hwang Gwanseck, Oh Minjun, Choi Jin, Kwon Geonwoo, Oh Ahyeon, Hwang Inyoung
Key words: Housing Market, Home Buying Decision Marking, Machine Learning, Agent Based Model, Behavioral Economics, Risk Aversion Measure, Self-Correcting Pattern, Herding Behavior
The purpose of this study is to identify the decision-making structure of home buyers in accordance with the changes in the housing market environment and its policies and establish a housing market analysis system reflecting this. The framework of this study is explained as follows. The beginning stage of this study is the consideration of the types of home buyers and their decision making structures. The existing literature and policy review reveals that the types of home buyers are becoming more diverse and subdivided, and that the behavioral approach to the decision making structures of the consumers is also becoming very important along with the traditional approaches.
Based on these contents, some empirical analyses and model construction are carried out at the main stage. It can derive the risk aversion measure according to the type of home buying consumers, explain the high proportion of housing assets in Korean households, and reveal the sustainability of housing demand
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depending on cohorts, although the growth rate of population and households slows down in Korea. Through structural changes in housing demand, the role of investment demand during the period of price increase is revealed, and the existence of downward rigidity and herding behavior in the housing market is explained. We establish a machine learning model and an agent-based model by reflecting the results of these empirical analyses as much as possible.
The final conclusion will suggest ways to establish a housing market analysis system. It will also excavate and present the monitoring indicators and policy indicators to conduct behavioral analysis, demand analysis, and investment sentiment analysis, of the existing trend analysis system, and suggest a market analysis system using the machine learning and the agent-based model along with the existing models such as the simultaneous equation model and the time series model of prediction and forecast system by some examples.
It is necessary to upgrade the housing market analysis system by reflecting the fact that the decision making structure of the home purchasing consumers is changing and subdivided. In this regard, the following policy measures were presented. First of all, the analysis of housing market transition effects, the reform of the regulation areas regarding the development of housing market monitoring indicators, the development of price indicators reflecting the phenomenon of price pull, the development of investment sentiment index in the housing market, the development of bubble index comparable to major overseas cities, and the reinforcement of housing demand investigation analysis system to analyze the behavior of housing consumers, the improvement of way of analysis of housing demand and the reform of housing supply and demand index were suggested.
SUMMARY ․ 205 In addition, regarding the utilization of machine learning and agent-based models, a method of predicting the housing market for the next 1 to 6 months using the machine learning model applying time difference and a method of simulating housing finance variables using the actor-based model were proposed.
This study contributed to the fact that it analyzed the behavior existing in the housing market in various aspects and to the point that it expanded the framework of awareness to looking at the phenomenon existing in the housing market of this country in a balanced manner in terms of supply and demand.
However, some more advanced studies that complement the limitations of this study are expected. Some advanced researches are needed regarding the expansion of research related to risk aversion measure, the definition and identification of demand behaviors such as actual demand, fake demand, investment demand and speculative demand, considering the dynamic relationship between the housing market and the rental market.
