What are tenants demanding the most? A machine learning approach for the prediction of time on market

Abstract

Purpose

This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.

Design/methodology/approach

The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.

Findings

Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.

Practical implications

The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.

Originality/value

Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

Citation

Cajias, M. and Freudenreich, A. (2024), "What are tenants demanding the most? A machine learning approach for the prediction of time on market", Journal of Property Investment & Finance, Vol. 42 No. 2, pp. 151-165. https://doi.org/10.1108/JPIF-09-2023-0083