雖然有許多研究重視房價的預測，但先前文獻多著重於觀察六都的房屋市場。但我們發現嘉義市在2012年房價指數僅約75，而2024年已經高達到165；另外，嘉義縣亦呈現房價指數高漲的趨勢，從2012年85成長至2024年195，顯示嘉義市、縣房屋市場蓬勃發展，這也是本文的研究起始動機，我們著重觀察嘉義縣、市房價的變化趨勢，並使用深度學習技術建構房屋價格的預測模型。本文與過去使用深度學習技術預測房價不同之處，乃在本文不是使用過去文獻採用的房價指數當為預測應變數；也不是只著重使用總體經濟、貨幣政策、房地產、人口結構變數當為預測自變數。本文資料是採用內政部不動產交易實價房屋資訊平台，我們以實價房屋交易的相關變數來預測嘉義市、縣房價，模型也會加入總體經濟變數、與房屋位置相關的人口等資訊。
本文研究時間涵蓋 2012年1月至 2024 年 12 月，房屋型態包含透天厝、住宅大樓、華廈、公寓、與套房等五種不同類型，實價房屋交易共61376筆資料，其中嘉義市31840筆、嘉義縣29536筆。我們以長短期記憶(LSTM)模型、與LSTM-滾動視窗兩種模型，分別預測五種不同類型的房價。研究結果顯示，以2012到2024整體資料來看，不論是嘉義市或嘉義縣，標準LSTM模型會比LSTM–滾動視窗模型還要來的準確。另外，從嘉義縣、市房屋在 2018 年前與 2018 年後之每坪單價與成交總價預測 RMSE 結果比較，發現在2018 年後，當各類房型的房價明顯上升情況下，LSTM–滾動視窗模型的預測表現是優於LSTM 模型，隱含著LSTM–滾動視窗模型在房價高波動時期預測嘉義縣、市房價，會比LSTM模型佳。

This study applies deep learning techniques to predict housing prices in Chiayi City and Chiayi County, Taiwan—regions that have experienced rapid price growth but remain underexplored in the housing literature. Using 61,376 transaction-level observations from the Ministry of the Interior’s Actual Price Registration System covering 2012–2024, this research incorporates detailed housing characteristics, macroeconomic indicators, temporal factors, and population-related variables. Long Short-Term Memory (LSTM) models and LSTM models with a rolling window mechanism are employed to forecast unit prices per ping and total transaction prices across five housing types. Forecast performance is evaluated using the Root Mean Square Error (RMSE). The results show that standard LSTM models perform better over the full sample period, while LSTM models with rolling windows achieve superior accuracy after 2018, when housing prices became more volatile. These findings suggest that rolling window approaches enhance model adaptability in rapidly changing housing markets and provide useful insights for regional housing policy and market risk assessment.

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