市地重劃對都市景觀格局與房價之影響

市地重劃（urban land readjustment）在臺灣被視為是實踐都市計畫的最重要工具。重劃後的地區格局方正故能促進更有效的建築使用，且具有一定綠地面積，除改善居住環境品質，更促使不動產價值有所提升。本研究目的在於比較重劃區與非重劃區之都市景觀格局差異，了解不同時期之市地重劃政策對於空間格局及綠地格局變化之影響，並研究空間格局及綠地格局對不動產價格之影響。根據內政部統計處（2020）資料顯示，臺灣六都中市地重劃面積以高雄市、臺中市及臺南市最大，並以臺中市人口成長速度最快，且於都市計畫中明訂須以市地重劃方式進行開發，因此本研究以臺中市都市計畫區為研究範圍。根據過去學者及都市計畫、社會發展背景，將都市地區劃分為三個種類：住宅興闢期重劃區（1980年代以前）、都市設計期重劃區（1980年代以後）及非重劃區。研究分為兩部分進行：第一部分為分析三種類地區的空間格局及綠地格局，利用臺中市都市計畫區目前最新2018年的土地使用分區圖為資料，透過Fragstats 4.2以街廓為塊區單元進行空間指數計算，探討並比較三者間的空間格局異同；第二部分為分析2019年研究範圍內的不動產交易標的周邊500公尺內的空間格局及綠地格局，利用不動產交易實價登錄資料，同樣以Fragstats 4.2以街廓為塊區單元進行空間指數計算，探討哪些空間格局將對房價造成影響。研究結果顯示，在空間格局部分，三種類地區在街廓平均面積、密度、平均距離（代表道路平均寬度）上有顯著差異，然而僅都市設計期重劃區的街廓平均面積及平均距離顯著大於住宅興闢期重劃區與非重劃區，且街廓密度顯著低於住宅興闢期重劃區與非重劃區，在住宅興闢期重劃區與非重劃區之間並無顯著差異，本研究推論住宅興闢期重劃區與非重劃區間無顯著差異之原因，可能與住宅興闢期重劃區以高密度的方式進行空間規劃因而整體空間遭切割，同時非重劃區因未經規劃而呈現較破碎的空間有關。在綠地格局部分，三種類地區在綠地比例、平均面積上有顯著差異，然而僅有都市設計期重劃區的綠地平均面積顯著大於住宅興闢期重劃區，本研究認為這可能與臺中市政府於非重劃區內積極規劃環狀綠園道系統有關，導致非重劃區內綠地平均面積增加所致。另外，多元迴歸分析顯示，街廓平均面積、綠地平均面積、綠地數量對房價有顯著影響力，當街廓平均面積越大或綠地平均面積越大、數量越多時，都將使得房價上升。

關鍵字

市地重劃；空間指數；都市景觀格局；綠地；特徵價格法

並列摘要

Urban land readjustment is considered as an important tool to achieve practicing urban planning. The regional landscape pattern will be more effective in building use, and will have more green spaces after readjusting. Urban land readjustment will not only improve the living environmental quality, but will also raise the housing price. Thus, there are two aims of the study. First, to compare the urban landscape pattern between readjusted area and non-readjusted area, to understand whether land readjustment policy change the block spatial pattern and green space spatial pattern or not. Second, understanding which block spatial pattern and green space spatial pattern will affect housing price. According to the statistics of Taiwan’s Ministry of the Interior (2020), Kaohsiung, Taichung, and Tainan have the largest urban readjested area. Among them, Taichung has the fastest population growth, and also clearly stipulated in the urban planning that development must be carried out by urban land readjustment. Thus, Taichung urban planning district were selected as research area. Based on past scholars, urban plans, and social background, we separate urban space into three parts: Residential Development Period Readjusted Area (before 1980s), Urban Design Period Readjusted Area (after 1980s), Non-Readjusted Area.The study was divided into two stages. First stage aims to analyze and compare the block spatial pattern and green space spatial pattern of the three different parts of urban space. This stage will use Taichung City Urban Plan 2018’s land use zoning map as data, use block as patch to analyze landscape pattern by Fragstats 4.2. Second stage aims to analyze the block spatial pattern and green space spatial pattern around the house within 500 m, to know which landscape pattern will affect housing price. This stage will use the 2019 actual price registration data from Taiwan’s Ministry of the Interior, also use block as patch to analyze landscape pattern by Fragstats 4.2. The result shows that block spatial pattern, including AREA_MN, PD, ENN_MN(means average road width), are significant differences in three parts. However, only AREA_MN and ENN_MN of blocks in Urban Design Period Readjusted Area are significantly larger than Residential Development Period Readjusted Area, and PD of blocks is significantly lower than Residential Development Period Readjusted Area and Non-Readjusted Area. There’s no significant difference between Residential Development Period Readjusted Area and Non-Readjusted Area. It may be related to the high-density planning of space in Residential Development Period Readjusted Area, which cuts the space, and Non-Readjusted Area hasn’t been planned so the space is fragmented. On the other hand, it has significant difference in PLAND and AREA_MN of green space pattern in three parts, but only AREA_MN significantly larger than Residential Development Period Readjusted Area. It may be related to Taichung government actively planned a circular green parkway system in Non-Readjusted Area, thus increasing the AREA_MN of green spatial pattern. In the part of housing price factors, the result shows that block’s ENN_MN, and green space’s AREA_MN significantly affect the unit price. It means wider roads and bigger green unit will raise house prices.