What question did this study set out to answer?

The aim is to create a framework for accurately predicting the condominium price index using diverse data sources.

July 4, 2026Real Estate0 citationsOpen Access

Predicting a Housing Price Index: A Two-Stage Machine Learning Approach Using Linked Micro-, Socio- and Macroeconomic Data from Frankfurt am Main

JSJ. SchmidTechnical University of Darmstadt

Key Points

The aim is to create a framework for accurately predicting the condominium price index using diverse data sources.
Developed a two-stage machine learning model using historical market and socioeconomic data.
Stage 1 estimated prices per square meter using techniques like random forest and ordinary least squares.
Stage 2 combined price predictions with macro-financial indicators to forecast the city-wide index.
The model achieved a relative root mean squared error of 2.25% and a mean absolute percentage error of 1.85%.
It outperformed a naïve persistence benchmark by decreasing root mean squared error by 23%.
Price persistence influenced stage 1 predictions, while lagged economic indicators impacted stage 2 forecasts.

Abstract

This study develops and evaluates a two-stage machine learning framework for forecasting the condominium price index of pre-pandemic market data of Frankfurt am Main, Germany, one quarter ahead. To the best of the author’s knowledge, it is the first study to combine German micro-level transaction and listing data, socioeconomic variables and macro-financial indicators in a single residential price-forecasting framework. Furthermore, it provides the first evidence on machine learning-based transaction price index forecasting in Germany. Methodologically, the framework links disaggregated and aggregate forecasting. In stage 1, prices per square metre are estimated for four market segments using ordinary least squares, random forest, extreme gradient boosting, and a stacked ensemble in a strictly out-of-sample expanding-window design. In stage 2, these predictions are combined with lagged index values and macro-financial indicators to forecast the city-wide index. The stage 2 model achieves a relative root mean squared error of 2.25% and a mean absolute percentage error of 1.85%, outperforming a naïve persistence benchmark by reducing root mean squared error by 23%. Model interpretation indicates that price persistence dominates stage 1, reflecting market inertia, while lagged macro-financial variables and location quality composition drive index forecasts, pointing to delayed financial market transmission and heterogeneous submarket dynamics.

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