# A Data-Driven Analysis on the Impact of High-Speed Rails on Land Prices in Taiwan
## 1 Introduction
In addition to these demand-side factors, house prices will be determined by the available supply, which is an aggregation of the number of sellers and the volume of new builds. Within a country or even within a city, property prices can vary significantly across specific locations depending on factors such as access to urban areas and proximity to amenities. Since HSR significantly enhances the locational accessibility, it can potentially increase the property prices in neighboring areas surrounding the HSR stations. This study seeks to contribute to the understanding of the associations between the introduction of HSR and land prices in Taiwan under the considerations of a spectrum of societal, economical, and infrastructural representative elements including population sizes, economic growth, lending rates, road densities, presence of an airport, provision of public goods such as education, etc. Specifically, the study quantified the impact of the introduction of HSR on land prices in the cities along HSR lines in the period 2005-2016 inclusive. A single-line network with only two primary stages of development provided the boundaries for the scope of the analysis. Based on extensive published data, the directional flows of the representing elements into the societal, economical, and infrastructural stocks were investigated by means of system dynamics (SD). A series of hypotheses on the interactions among these elements was represented in a stock and flow diagram. While SD is an approach to depicting the behavior of complex systems over time, the analysis was supplemented with multivariate regression that determined the relative impact of the elements categorized broadly into the HSR's accessibility, social relevance, economic growth, and residential infrastructure on land prices. Subsequently, recognizing the impact of HSR on property prices is also dependent upon the stage of economic, social, and infrastructure development, and principal component analysis (PCA) was used to extract the underlying structure of the system. In view of the large datasets from the large number of elements considered, PCA was the preferred adaptive technique for reducing the dimensionality of the datasets and increasing interpretability while minimizing information loss by creating new uncorrelated dimensions (i.e., the principal components) that successively maximized variance. The principal components were reduced to solving an eigenvalue/eigenvector problem, and the new dimensions were defined by the dataset at hand. The statistical significances and association levels of the elements that were obtained indicated the tradeoff between each element and its association with land prices. The interactive relationships among ttthe presence of HSR and other dimensions are helpful to the authorities iin devising ways to curb spikes in property prices while benefiting from the improved accessibilities brought about by HSR. By having a clearer understanding of the structure of the system over the 12 year period from 2005-2016, it will also improve the prediction of how land prices will change in response to the economic, societal, and infrastructural developments of the cities in Taiwan.