Predicting House Price Indices: A Machine Learning Approach Using Linked Listing and Transaction Data

Jan Schmid, He Cheng, Francisco Amaral and Zdrzalek Jonas

ERES from European Real Estate Society (ERES)

Abstract: This study proposes the conception of a real estate market forecasting model for the research area of Frankfurt am Main, utilising sophisticated AI algorithms to predict house price indices. The model integrates two primary datasets: listing data from ImmoScout24 and transaction data from the local expert committee. These datasets were linked using a threshold optimisation approach to ensure accurate matching of listings and transactions at the object level. A comprehensive review of prior studies was conducted to select key predictors, supplemented by novel variables that measure differences between listing and transaction data, such as price differences and time on market. The Random Forest based algorithm selection process involved a meta-learning approach of 54 prior studies, adapted to the final dataset's structure. The XGBoost model was selected as the most suitable algorithm, achieving a Mean Absolute Percentage Error (MAPE) of 1.76% and a Root Mean Square Error (RMSE) of 2.43 on the testing dataset. The methodology also incorporated macroeconomic and socio-economic indicators, with data structured into spatial-temporal grids for quarterly forecasting. The model demonstrated high predictive accuracy, offering valuable insights for real estate market analysis and future decision-making. Subsequent research is planned to validate the model and apply it to additional urban regions, initially focusing on the seven largest cities in Germany.

Keywords: Forecasting; House Price Indices; Real Estate Market; XGBoost (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2025-01-01
New Economics Papers: this item is included in nep-big and nep-for
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