傳統不動產折舊觀點認為，建築物隨屋齡增加，因物理耗損與功能退化而逐漸產生價值減損；然而，歷史舊城區之不動產價格亦可能受到土地、區位、歷史文化及老屋再利用等因素共同作用，使屋齡與價格未必呈現單純線性下降。既有研究多著重屋齡之折舊效果，即使納入平方項考量非線性關係，仍較少進一步辨識房價是否由折舊轉為逆折舊、何時發生反轉，以及不同條件與反轉特徵之關聯。基於此，本研究以臺南市中西區透天建築為研究對象，探討「前期折舊、後期逆折舊」之房價逆折舊現象及價格反轉點，並分析不同建物、基地、區位與文化環境條件之關聯。
本研究採用2013年至2025年臺南市中西區1,384筆透天建築交易資料，整合建物與基地、區位環境、歷史街區、潛在危老建築及文化觀光活動等資料，並利用POI與加權高斯核密度估計建構文青活動及觀光強度指標。實證分析先以OLS、Moran's I及GWR檢視整體價格關係與空間異質性，再建立貝氏房價逆折舊二次曲線模型，將二次曲線分解為新成屋價格水準、初始折舊速度及逆折舊曲率。透過貝氏推論處理多項曲線參數與解釋變數共同作用之模型結構，並利用MCMC取得參數之聯合後驗分布，使價格反轉之判定不僅依賴單一係數點估計，而能進一步納入初始折舊速度與逆折舊曲率之共同不確定性，據以推估房價反轉型態之後驗機率與反轉屋齡。
實證結果顯示，臺南市中西區透天建築存在「前期折舊、後期逆折舊」之非線性價格型態。基準模型之初始折舊速度呈明確負向、逆折舊曲率呈明確正向，房價反轉型態後驗機率為100%，反轉屋齡後驗中位數為35.31年，95%等尾區間介於32.06年至40.21年。進一步納入個別樣本條件後，1,384筆樣本中共有774筆之房價反轉型態後驗機率達95%以上，占55.92%；其反轉屋齡中位數為37.21年，且74.16%集中於30至50年間，顯示房價逆折舊並非所有高屋齡建築皆具有之固定現象，價格反轉點亦具有明顯差異。
不同條件與房價逆折舊二次曲線亦呈現不同關聯。土地面積、建物面積及臨主要道路與較高之新成屋價格水準呈明確正向關聯；歷史街區核心區、緩衝區及潛在危老建築與較平緩之初始價格下降幅度具有明確關聯。逆折舊曲率方面，文青活動指標呈明確正向關聯，而歷史街區核心區、緩衝區及潛在危老建築則呈明確負向關聯。在其他條件固定下，低、中、高文青活動指標之代表性反轉屋齡分別約為40.5年、36.7年及34.0年，顯示文青活動程度提高與較大的逆折舊曲率及較早之價格反轉點相關，亦說明歷史街區區位身分與街區實際文化活動具有不同的價格關聯。
綜合而言，本研究所辨識之房價逆折舊並非「屋齡越高，價格必然越高」，而是部分透天建築由前期價格下降逐漸轉為後期回升之價格反轉現象。本研究之主要貢獻，在於將傳統屋齡折舊研究由「折舊程度」推進至「逆折舊是否成立、何時反轉及哪些條件與反轉特徵相關」，並透過貝氏聯合後驗分布量化房價反轉型態之支持程度與價格反轉點；同時利用POI衡量街區實際文化活動，進一步區分歷史街區區位身分與文化活動之不同關聯。研究結果可提供歷史舊城區不動產價格研究，以及老屋保存、活化與更新政策之實證參考。

Traditional depreciation theory assumes that property values decline with building age. However, in historic urban districts, land, location, cultural resources, and adaptive reuse may produce a nonlinear age–price relationship. This study examines whether townhouses in Tainan’s West Central District exhibit “inverse depreciation,” characterized by initial price decline followed by an upward reversal, and investigates the conditions associated with this pattern.
Using 1,384 townhouse transactions from 2013–2025, this study integrates building, land, locational, historic-district, and cultural-tourism characteristics. OLS, Moran’s I, and Geographically Weighted Regression (GWR) are used for global and spatial diagnostics. A Bayesian quadratic age–price model is then developed to distinguish the new-property price level, initial depreciation rate, and inverse-depreciation curvature. MCMC sampling is used to estimate the posterior probability and timing of price reversal.
The results support a nonlinear pattern of initial depreciation followed by inverse depreciation. The baseline model shows a 100% posterior probability of price reversal, with a median turning age of 35.31 years. Among the 1,384 observations, 774 (55.92%) have at least 95% posterior support for this pattern, with a median turning age of 37.21 years. Historic core and buffer-zone locations are associated with milder initial depreciation but weaker inverse-depreciation curvature, whereas hipster activity is positively associated with curvature.
These findings show that inverse depreciation is not a universal effect of aging but a heterogeneous price-reversal process. The proposed Bayesian framework advances conventional depreciation analysis by identifying whether reversal occurs, when it occurs, and how different conditions are associated with its characteristics.

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