Abstract
The main purpose of this paper is to use regression models to explore the factors affecting housing prices as well as apply spatial aggregation to explore the changes of urban space prices. This study collected data in Taitung City from the year 2013 to 2017, including 3533 real estate transaction price records. The hedonic price method, spatial lag model and spatial error model were used to conduct global spatial self-correlation tests to explore the performance of house price variables and space price aggregation. We compare the three models by R² and Akaike Information Criterion (AIC) to determine the spatial self-correlation ability performance, and explore the spatial distribution of prices and the changes of price regions from the regional local indicators of spatial association spatial distribution map. Actual analysis results show an improvement in the ability to interpret real estate prices through the feature price mode from the R² value assessment, the spatial delay model and the spatial error model. Performance from the AIC values show that the difference of the spatial delay model is smaller than that of the feature price model and the spatial model, demonstrating a better performance from the space delay model and the spatial error model compared to the feature price model; improving upon the estimation bias caused by spatial self-correlation. For variables affecting house pricing, research results show that Moran’s I is more than 0 in real estate price analysis over the years, all of which show spatial positive correlation. From the LISA analysis of the spatial aggregation phenomenon, we see real estate prices rise in spaces surrounded by high-priced real estate contrast with the scope of space surrounded by low-cost real estate shifting in boundary over the years due to changes in the location and attributes of real estate trading transactions. Through the analysis of space price aggregation characteristics, we are able to observe the trajectory of urban development.
Highlights
Real estate should be treated no differently than any other commodity—that is, a combination of usefulness and exchange value
Taitung City was adopted as the scope of empirical analysis
A total of 11 variables in three categories were analyzed using a hedonic price method, spatial lag models (SLM), and spatial error models (SEM) to determine the factors of real estate prices in Taitung County
Summary
Real estate should be treated no differently than any other commodity—that is, a combination of usefulness and exchange value. Real estate prices are affected by a myriad of complex factors, such as overall environmental factors and independent environmental factors, as described by Wu [1] and Wang [2]. These factors include income, national savings, money supply, supply-side policy, trade volume, and lending rates. Studies on independent environmental factors explore the effects of regional environments on real estate These factors include access to public facilities, location, environment, convenience of transportation, residential density, household characteristics, housing type, purpose, size, number of rooms, age of building, and floor level [3,4,5,6,7,8]
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