台灣在近年來房價不斷上升是有目共睹的現象，而根據內政部營建署公告之111年第1季房價負擔能力統計發布內容，全台房價所得比為9.58倍，相對於國外有著較高的比例且在各城市間又有極大的差異，由此可見台灣可能存在著住宅負擔能力分布不均且不足的問題。本研究欲透過相關資料並以空間統計方法，探討全台各村里之可負擔房價與住宅負擔能力空間分布差異。
本文使用2018至2019年內政部不動產實價登錄交易資料以及綜合所得申報之家戶所得推算全台各村里之貸款負擔率以衡量住宅負擔能力狀況。並透過空間自相關分析方法，觀察與分析各村里家戶住宅負擔能力以及不可負擔房屋單價的空間分布，再以地理加權迴歸模型（GWR）探討影響區域住宅負擔能力差異的相關因素。
研究結果顯示不可負擔房屋單價在六都均具有空間自相關的現象，且多集中在改制前的舊市區，以區域型空間自相關來看發現在各都市不可負擔房屋單價的熱區內仍有較低住宅負擔壓力的村里，此外在舊台中市的內部住宅負擔能力問題也具有空間的異質性，透過地理加權迴歸模型探討住宅負擔能力問題較OLS表現較佳，調整後R2達到0.77，對住宅負擔能力有顯著影響的變數有屋齡、建築面積及老化指數等非距離因素，以及轉運站距離、文化設施距離、百貨公司距離、工廠距離、金融設施距離及捷運站距離等距離因素，各影響變數對住宅負擔能力問題的影響在空間上均具有差異性，因此建議在規劃與實施解決住宅負擔能力問題的政策時，應將空間的相關影響因素考慮進去，才可針對不同的區域規劃以及住宅政策的制定進行合理的調整。

In recent years, it is obviously to say that housing price in Taiwan has been rising continuously. According to the housing affordability statistic by Construction and Planning Agency in Q1 2022, Price to Income ratio（PIR）was 9.58, which is a relative high ratio compared to Foreign countries. There are also great differences among Taiwan cities, which shows that Taiwan may have the problem of uneven distribution and insufficient housing affordability. This study intends to explore the differences in the spatial distribution of affordable housing prices and housing affordability in villages across Taiwan through the use of relevant data and spatial statistical methods.
This thesis uses real estate transaction data and the household income to calculate the Mortgage Burden in each village in Taiwan to measure the housing affordability from 2018 to 2019. And through the spatial autocorrelation analysis method, observe and analyze the spatial pattern of housing affordability and unit price of unaffordable housing in each village, finally using the geographically weighted regression model (GWR) to explore the relevant factors that affect the difference of the spatial distribution of regional housing affordability.
The research results show that the unit price of unaffordable housing has spatial autocorrelation in the six Municipalities, and most of them are concentrated in the old urban areas before the restructuring. Through regional spatial autocorrelation analysis, it is found that there also have lower housing affordability pressures in the hot spot of unaffordable housing unit price in each city. In addition, the internal housing affordability problem in former Taichung city also has spatial heterogeneity. Using GWR to explore the housing affordability problem performed better than OLS, and the adjusted R2 reached 0.77. Factors that have a significant impact on housing affordability can be categorized into non-distance and distance-related variables. The former includes housing age, building area, and aging index, while the latter comprise proximity to transit stations, cultural facilities, department stores, factories, financial institutions, and MRT stations. The influence of each impact factors still has spatially difference. Therefore, it is recommended to incorporate spatial factors when formulating and executing policies to address housing affordability concerns, leading to more tailored regional planning and housing strategies.

中華民國住宅學會（2010）。內政部營建署98年度房價負擔能力計算方式與國際各國比較總結報告。內政部營建署委託之專題研究成果報告（案號：98A-060）。
王景澤（2015）。住宅負擔能力、自備款資金與消費行為關係之研究（碩士論文）。 國立政治大學地政學系碩士在職專班。
內政部營建署（2023）。國泰房地產指數（可能成交價指數）。內政部不動產資訊平台。取自：https://pip.moi.gov.tw/V3/E/SCRE0201.aspx
台中市政府（2002）。變更台中市都市計畫（軍功、水景里地區）細部計畫（第一次通盤檢討）說明書。台中市政府。
李文傑、甯方璽、黃雅祺（2014）。集合式住宅房價泡沫形成之研究-以台中市為例。建築與規劃學報，15（2/3），155-174。
林立爵（2018）。購屋負擔壓力之空間自相關分析-以台灣都市為例（碩士論文）。國立成功大學都市計劃研究所。
林祖嘉與林素菁（1993）。台灣地區環境品質與公共設施對房價與房租影響之分析。住宅學報，（1），21-45。
花敬群與林秋瑾（2013）。「101年辦理房價負擔能力資訊統計分析與發佈」委託專業服務後續擴充（102年度）案民國102年第3季報告書。內政部營建署委託之專題研究成果報告（案號：102A-057）。
張金鶚、高國峰、林秋瑾（2001）。台北市合理房價-需求面分析。住宅學報，10（1），51-66。
張嘉玲（2004）。台中市都市空間體系的建構與擴展（碩士論文）。國立成功大學建築研究所。
許家偉（2023）。台中重劃區-田地記事【部落格文字資料】。取自https://coco080a0.wordpress.com/。
彭建文與蔡怡純（2017）。人口結構變遷對房價影響分析。經濟論文叢刊，45（1），163-192。
曾平毅（1994）。都會區擁屋力之研究（博士論文）。國立交通大學交通運輸研究所。
馮容莊（2009）。資訊資料品質與管理。護理雜誌，56（3）， 29-35。
黃素香（2014）。台中捷運綠線對房價的影響（碩士論文）。逢甲大學財稅學系在職專班。
楊宗憲與蘇倖慧（2011）。迎毗設施與鄰避設施對住宅價格影響之研究。住宅學報，20（2）， 61-80。
蔡明哲（2015）。台灣都會區氣候條件與空氣品質之價值評估
－特徵價格法之應用（碩士論文）。國立臺北大學自然資源與環境管理研究所。
蔡國士（2010）。都市成長的烏托邦「或」空間資本的競技場：台中市豐樂重劃區的空間生產（碩士論文）。國立彰化師範大學地理學系。
鄧依萍（2004）。台灣地區房屋購買力指數分析與家戶特徵對購屋能力影響之研究（碩士論文）。 國立政治大學財務管理研究所。
薛立敏（1996）。台灣地區合理房價之探討。台北市：中華經濟研究院。行政院經濟建設委員會。
Aluko, O. (2011). The effects of location and neighbourhood attributes on housing values in metropolitan Lagos. Ethiopian Journal of Environmental Studies and Management, 4(2), 69-82.
Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical analysis, 27(2), 93-115.
Basu, S., & Thibodeau, T. G. (1998). Analysis of spatial autocorrelation in house prices. The Journal of Real Estate Finance and Economics, 17(1), 61-85.
Bourassa, S. C. (1996). Measuring the affordability of home-ownership. Urban studies, 33(10), 1867-1877.
Bramley, G. (1990). Access, affordability and housing need. School for Advanced Urban Studies, University of Bristol.
Bunting, T., Walks, A. R., & Filion, P. (2004). The uneven geography of housing affordability stress in Canadian metropolitan areas. Housing studies, 19(3), 361-393.
Cellmer, R., Cichulska, A., & Bełej, M. (2020). Spatial analysis of housing prices and market activity with the geographically weighted regression. ISPRS International Journal of Geo-Information, 9(6), 380.
Cellmer, R., & Trojanek, R. (2019). Towards increasing residential market transparency: Mapping local housing prices and dynamics. ISPRS International Journal of Geo-Information, 9(1), 2.
Chen, Y., Liu, X., Li, X., Liu, Y., & Xu, X. (2016). Mapping the fine-scale spatial pattern of housing rent in the metropolitan area by using online rental listings and ensemble learning. Applied Geography, 75, 200-212.
Conway, D., Li, C. Q., Wolch, J., Kahle, C., & Jerrett, M. (2010). A spatial autocorrelation approach for examining the effects of urban greenspace on residential property values. The Journal of Real Estate Finance and Economics, 41(2), 150-169.
Fotheringham, A. S., Charlton, M. E., & Brunsdon, C. (1998). Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and planning A, 30(11), 1905-1927.
Geng, J., Cao, K., Yu, L., & Tang, Y. (2011). Geographically Weighted Regression model (GWR) based spatial analysis of house price in Shenzhen. 2011 19th international conference on geoinformatics,
Getis, A., Ord, J. K. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical analysis, 24, 189-206.
Goodchild, M. F. (1986). Spatial Autocorrelation. Geo Books, Norwich.
Huang, Z., Chen, R., Xu, D., & Zhou, W. (2017). Spatial and hedonic analysis of housing prices in Shanghai. Habitat International, 67, 69-78.
Ismail, S. (2006). Spatial autocorrelation and real estate studies: A literature review. Malaysian Journal of Real Estate, 1(1), 1-13.
Jewkes, M., & Delgadillo, L. (2010). Weaknesses of housing affordability indices used by practitioners. Journal of Financial Counseling and Planning, 21(1).
Kang, S. Y., McGree, J., & Mengersen, K. (2013). The impact of spatial scales and spatial smoothing on the outcome of Bayesian spatial model. PLoS One, 8(10), e75957.
Li, X., & Zhang, C. (2021). Did the COVID-19 pandemic crisis affect housing prices evenly in the US? Sustainability, 13(21), 12277.
Linneman, P. D., & Megbolugbe, I. F. (1992). Housing affordability: Myth or reality? Urban studies, 29(3-4), 369-392.
Maclennan, D., Gibb, K., & More, A. (1990). Paying for Britain's housing. Joseph Rowntree Foundation.
Meen, G. (2011). A long-run model of housing affordability. Housing studies, 26(7-8), 1081-1103.
Mitchell, A. (2005). The ESRI Guide to GIS Analysis (Vol. 2: Spatial Measurements & Statistics). ESRI Press.
Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical analysis, 27(4), 286-306.
Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of political economy, 82(1), 34-55.
Shi, X., Li, M., Hunter, O., Guetti, B., Andrew, A., Stommel, E., Bradley, W., & Karagas, M. (2019). Estimation of environmental exposure: interpolation, kernel density estimation or snapshotting. Annals of GIS, 25(1), 1-8.
Silverman, B. W. (1986). Density estimation for statistics and data analysis (Vol. 26). CRC press.
Sirmans, S., Macpherson, D., & Zietz, E. (2005). The composition of hedonic pricing models. Journal of real estate literature, 13(1), 1-44.
Wilhelmsson, M. (2002). Spatial models in real estate economics. Housing, theory and society, 19(2), 92-101.
Xie, Z., & Yan, J. (2008). Kernel density estimation of traffic accidents in a network space. Computers, environment and urban systems, 32(5), 396-406.