FORECASTING HOUSING PRICES IN URBAN NANNING: AN APPLICATION OF TIME SERIES ANALYSIS
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Over the past few decades, Nanning has experienced significant urban
development and economic growth, in which the real estate sector has played
a vital role. Fluctuations in housing prices have had a profound impact on
economic stability and social welfare. This study aims to apply advanced
statistical time series methods to analyze the evolution of residential housing
prices in Nanning and to forecast future trends based on historical data. A
dataset comprising 175 data points of residential housing prices in Nanning over
the past 15 years was utilized. Six time series analysis methods were compared
to examine the characteristics of trend, cycles, and seasonality in housing
prices, leading to the development of predictive models.The findings indicate
that the Holt-Winters Method yields the lowest Mean Absolute Percentage Error
(MAPE) and Mean Absolute Deviation (MAD), suggesting it is the most suitable
time series forecasting method for predicting housing prices in Nanning. This
study offers empirical evidence to support the formulation of urban housing
policies by government authorities and provides valuable reference for investors and residents regarding future market trends, thereby contributing to
sustainable urban development and social equity.
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