Conference Agenda

There are two primary methods for estimating housing price indices: the hedonic regression approach and repeat sales. However, the hedonic regression approach can encounter problems such as multicollinearity and selecting appropriate explanatory variables. In Taiwan, only the hedonic regression method was available for estimating housing price indices until the implementation of the real-price registration system in July 2012. Unfortunately, many Taiwanese believe that property ownership leads to wealth accumulation, which often results in properties remaining unsold for 15-20 years. This makes it challenging to use the repeat sales method to compute a housing price index.

As most Taiwanese reside in apartment complexes, where the building types are similar, we propose a novel approach to estimate the housing price index. We suggest using similar cases to replace repeated transaction cases, which efficiently explores sample sizes and provides a robust index. This approach addresses the problem of sample selection bias in repeat sales and can be applied across the Asian region to estimate housing price indices.

The economic environment is full of uncertainty, and unexpected events often occur, causing market volatility. For example, the recent outbreak of COVID-19 pandemic has brought about significant uncertainty and caused widespread concerns about the future economic changes. This has had an impact on consumer expectations in the market and caused changes in the production sequence and output of producers. However, the government's response measures through monetary, fiscal, and housing policies have caused further market uncertainty. Therefore, this study aims to explore the above-mentioned issues. Firstly, we analyze the causes of uncertainty in the housing market. Secondly, we collect existing international uncertainty indices to understand their compilation methods. Lastly, we construct our own housing market uncertainty index using keyword search method to analyze the impact of uncertainty on the supply and demand sides of the housing market, i.e., how uncertainty affects transaction indicators such as housing prices, transaction volume, time-on-the-market, and price concession. We will employ Granger causality test to analyze the relationship between uncertainty and the housing market, in order to gain a better understanding of the impact of uncertainty on the housing market and provide insights for academic researches and the formulation of relevant housing policies by the government.

In contrast to the previous literature focusing on the spatial correlation of housing prices, this paper concentrates on the spatial aggregation of the quantity of housing transactions and analyzes the impact of transaction hotspot areas on housing prices. In addition, considering the negative influence of speculating behaviors in the housing market, the spatial distributions of short-term flipping are employed. The empirical results for Taipei City show that after controlling for the effects of spatial correlation and individual housing attributes, the impact of transaction hotspot areas on housing prices is significantly negative, while the impact of flipping hotspot areas is significantly positive. The lower the housing price, the greater the abovementioned effects are. The results verify that the key to driving up housing prices lies in flipping behaviors. Low-priced houses are more susceptible to the effects of spatial aggregation of trading volume in the real estate market. The government should not be overly concerned about whether the housing market is hot or not but should pay attention to short-term repeated transactions.