Rental Market Tightness in Ile-de-France: Empirical evidence from platform data

Martin Regnaud and Julie Le Gallo

ERES from European Real Estate Society (ERES)

Abstract: The paper investigates the dynamics of rental market tightness in Ile-de-France, leveraging user-generated data from the SeLoger platform to provide near-real-time insights.Using an original dataset of over 85,000 unique rental listings from April 2023 to June 2024, it introduces three novel indicators—email alerts, contact requests, and transformation rates—to capture renters preferences and search intensity. The analysis identifies key market drivers, highlighting intense demand for small, unfurnished apartments, and emphasizes how rising mortgage rates since 2022 have exacerbated competition in the rental market. Employing machine learning models with explainability techniques, the study reveals the interplay of variables related to apartment characteristics, economic conditions, and spatial context in shaping market tightness. The findings highlight the importance of accounting for platform-specific effects, such as listing visibility and listing quality, to achieve an accurate understanding of market dynamics. This research contributes to understanding housing market dynamics and informs policies aimed at mitigating rental market frictions.

Keywords: French Rental market; Housing Market Tightness; Machine Learning; Real Estate Market Platform (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
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