In the evolution of Taiwan's real estate market,
policies to curb soaring housing prices have always been a topic of close
concern to the government and academia. This study analyzes the relevant data
between the counties and cities in the municipalities between 2008 and 2023,
the relationship between the number of building and residential sales
transfers, the unit price per ping and the average total price of real estate,
and uses the number of building and residential sales transfers, the unit price
per ping and the average total price of real estate in Taiwan's municipalities
as the response variables; various real estate control policies are used as
independent variables; economic growth rate, annual growth rate of consumer
price index, average mortgage interest rate of the five major banks, weighted
index return rate, annual growth rate of money supply, regular salary, basic
salary, total population, and unemployment rate are used as control variables
for analysis to evaluate the effectiveness of different policies in achieving
the expected goals.
The analysis results show
that housing prices and transaction volumes in Taipei City have changed
significantly after the implementation of the real estate control policy. In
contrast, housing prices and transaction volumes in Tainan City are at the bottom
among the municipalities. The total population of Taoyuan City has a
significant impact on the real estate market. In addition, the correlation
coefficient between the Consumer Price Index and the number of building and
residential sales transfers is not high, and its direct impact on real estate
transaction volume is relatively weak. There is a positive correlation
coefficient between real estate prices in municipalities and actual income and
wage levels, indicating that increases in income and wage levels will promote
increases in housing prices. There are significant differences in the impact of
policy implementation on housing prices and transaction volumes in various
regions. These differences are mainly affected by factors such as local
economic development level, population mobility, and income and wage levels.
These findings can provide important reference for formulating more precise and
effective real estate regulation policies in the future, and emphasize the
importance of adapting to local conditions.
