鄰里環境與公共設施對房價與租金之影響 - 以臺北市為例

Abstract

隨著就業機會和人口漸趨集中於都市，居住需求也開始逐漸擴大，購屋與租屋族群日益增加，根據臺北市地政局針對房價與租金分布進行統計，發現價格分布趨勢並未呈現連動，推測租買族群在選擇居住地點之考量條件可能有所差異，故對價格之影響程度也有所不同。回顧文獻發現鄰里環境與公共設施為其重點考量因素，故將價格影響因素主要分為前述兩個面向進行探討。\n\n本研究使用2019年內政部實價登錄資料與591租屋網成交案件，保留建物類型為公寓及電梯大樓為研究樣本。以等量分配法（Quantile）將各里平均單價分組，劃分為高房價低租金鄰里與低房價高租金鄰里，再運用地理加權迴歸模型（Geographically Weighted Regression，GWR），分析鄰里環境與公共設施對於前述各鄰里之價格影響差異，並以地理資訊系統（Geographic Information System，GIS）視覺化展示各項特徵之空間分布情形。\n\n研究結果顯示，在價格分布方面，高房價地區未必為高租金地區，除信義區例外。在鄰里環境特徵與公共設施特徵對價格影響差異方面，高房價低租金鄰里，以所得、住宅竊盜案件數、空氣品質指標、與大學距離，共4項特徵最常對價格影響產生差異；低房價高租金鄰里，以住宅竊盜案件數、噪音、空氣品質指標、與捷運站距離、與醫院距離、與公園距離，共6項特徵最常對價格影響產生差異。值得注意的是，不論前者或後者分組之鄰里，住宅竊盜案件數及空氣品質指標此兩項特徵皆會造成分組過後之價格產生影響差異。

As employment opportunities and population tend to be concentrated in cities, housing demand has gradually expanded, and the number of buyers and renters is increasing. According to the statistics of the distribution of house prices and rents by the Taipei City Lands Bureau, it is found that the trend of price distribution does not show a link. The rent-buyers may have different considerations in choosing a place to live, so the degree of influence on the price is also different. Reviewing the literature found that the neighborhood environment and public facilities are the key consideration factors, so the price influencing factors are mainly divided into the aforementioned two aspects for discussion.\n\nThis study uses the real-price registration data of the Ministry of the Interior and the transaction cases of 591 renting a house in 2019. The types of buildings are apartments and elevator buildings as the research samples. The average unit price of each mile is grouped by the Quantile method, and divided into neighborhoods with high housing prices and low rents and neighborhoods with low housing prices and high rents. Then, using the geographic weighted regression model (GWR), the neighborhood environment and public facilities are analyzed for The price of the neighborhood affects the difference, and the geographic information system (GIS) is used to visualize the spatial distribution of various characteristics.\n\nThe research results show that in terms of price distribution, areas with high housing prices may not necessarily be areas with high rents, with the exception of Xinyi District. In terms of the difference between the environmental characteristics of the neighborhood and the characteristics of public facilities, the neighborhoods with high housing prices and low rents, including income, number of residential theft cases, air quality indicators, and distance from universities, have the most common impact on prices. In neighborhoods with high housing prices, the number of residential thefts, noise, air quality indicators, distance from the MRT station, distance from the hospital, and distance from the park, six characteristics most often have a difference in price effects. It is worth noting that regardless of the neighborhood in the former or the latter group, the two characteristics of the number of residential theft cases and the air quality index will cause the price difference after the grouping.