| 摘要: | 工業區的設立促進當地經濟成長，同時工廠的運營需要大量的人力，吸引許多人才到此求職，工業區的設立為當地經濟帶來就業機會，人口的移入與房地產市場息息相關，但工廠運作過程中也為周遭環境造成許多危害，故本研究將探討工業區對鄰近房地產價格造成的影響。
本研究房地產資料選自 2018 年 1 月 1 日至 2022 年 12 月 31 日台灣經濟新報 TEJ 之近五年不動產實價登入交易資料，工業區資料則根據經濟部工業局-台灣工業用地供給與服務資訊網《110 年度工業區管理開發年報》選出:中壢工業區、觀音工業區、龜山工業區、台中工業區、彰濱工業區、安平工業區、大發工業區、高雄臨海工業區以及林園工業區，共九個工業區作為本研究之工業區範圍。利用地理資訊系統(Quantum GIS)算出工業區與房地產直線距離四公里以內的房地產交易資料，並考慮房地產特徵屬性設定本研究之模型。
本研究使用對數特徵價格模型運用一般迴歸分析法與分量回歸分析法作為本研究之分析方法，一般分析法結果顯示中壢工業區、觀音工業區、彰濱工業區以及高雄臨海工業區符合本研究預期且顯著，距離工業區越遠則房價越高，分量回歸分析法討論一般迴歸分析結果符合本研究預期且顯著的四個工業區不同價格分位兩公里以內虛擬變數對房價的影響，結果顯示不同工業區在不同價格分為下兩公里以內虛擬變數對房價的影響會因工業區所在的地理位置而有所差異。;This study real estate data selected from January 1,2018, to December 31, 2022, sourced from Taiwan Economic Journal (TEJ) for the past five years. Industrial zone data were obtained from Taiwan Industrial Land Supply and Service Information Network, specifically from the "2021 Industrial Zone Management and Development Annual Report." The selected industrial zones for this study include Zhongli Industrial Zone, Guanyin Industrial Zone, Guishan Industrial Zone, Taichung Industrial Zone, Changhua Coastal Industrial Zone, Anping Industrial Zone, Dafa Industrial Zone, Kaohsiung Linhai Industrial Zone, and Linyuan Industrial Zone—totaling nine industrial zones. The study utilizes a Quantum GIS to calculate real estate transaction data within a four-kilometer diameter of industrial zones. Consideration is given to the characteristics and attributes of real estate in setting up the model for this study.
The study employs a logarithmic hedonic price model, utilizing both ordinary regression analysis and quantile regression analysis as the analytical methods. The results from the ordinary analysis indicate that Zhongli Industrial Zone, Guanyin Industrial Zone, Changhua Coastal Industrial Zone, and Kaohsiung Linhai Industrial Zone align with the study′s expectations and are statistically significant. The findings show that as the distance from the industrial zone increases, housing prices also tend to rise. The quantile regression analysis examines the impact of within-two-kilometer dummy variables on housing prices for the four industrial zones that exhibit expected and significant results in ordinary regression analysis. The results indicate that the impact of within-two-kilometer dummy variables on housing prices varies based on the geographical location of different industrial zones within distinct price segments. |
