Understanding rental profit and mechanisms that yields rental and real estate prices using machine learning approach

Martin Regnaud, Julie Le Gallo and Marie Breuille

ERES from European Real Estate Society (ERES)

Abstract: In 2020, MeilleursAgents was estimating that 2 years and 10 months were needed by a French household to amortize the cost of buying versus renting on average in France. At the same time, in Paris, the same household would have to wait 6 years and 11 months to amortize its costs. These figures are of utmost importance for households to help them decide whether they should buy or rent their main residence.From an operational perspective, estimating this time is made possible by a precise knowledge of rent to price ratios. The main objective of this contribution is estimating those ratios on the whole French territory using observations of rent and transaction prices for the same housing between 2010 and 2021. Once those ratios are estimated, we highlight the factors that determine them using machine learning methods.Using a coarsened exact matching, we estimate rent to price ratios on the whole territory. Then we compare two different approaches to identify the determinants of these ratios. The first approach consists in explaining the ratios using a linear regression model to predict them using housing characteristics and geographical amenities. The second approach uses a gradient boosting decision tree model to predict the ratios. Hence, we can explain the role of each feature of the model thanks to explainability methods associated with tree models: feature importance and shape values. In order to proceed with this study, we use rental listings from MeilleursAgents platform that have geolocation at the address level. This use of such listings is inspired by Chapelle&Eymeoud(2018)1 which shows that web scrapped listings are unbiased compared to survey data such as the ones from the “Observatoire Locaux des Loyers” (OL) in France. Moreover, these surveys are limited to certain dense areas whereas our study aims at comparing mechanisms on the whole French territory.These listings are matched with the national DV3F French database which provides us with a parcel geolocation level. Matching these two sources between 2010 and 2021 provides us with 85’000 matched ratio observations. The (rent, price) couples are used to estimate rent to price ratios and to highlight the differences in the influence of each factor depending on the territory but also thanks to our precise geolocation, inside urban areas.Our study has a double contribution. First, from a methodological point of view, using a gradient boosting model to estimate and explain rent to price ratios has never been done. The main advantage of this method compared to classic methods is a better handling of interactions and effect heterogeneity. The second contribution leans on the precise geolocation level of our observations. These ratios are most of the time studied using ratios of average rent and average prices because of the scarcity of precisely geolocated data. Yet, Hill&Syed(2016)2 showed that such an approximation can lead to an error up to 20% when estimating the ratio. Therefore, they advise to use housing level matching to control feature heterogeneity between rented and sold housing. Our study is thus the first study in France that allows an exact matching outside of the dense areas covered by the “Observatoire Locaux des loyers” on this topic.We highlight the strong heterogeneity of rent to price ratios inside dense urban areas but also at a larger scale. To our knowledge, this study is the first to bring this phenomenon out at this national scale.1. Chapelle G., Eymedoud J.-B., « Can Big Data Increase Our Knowledge of Local Rental Markets? Estimating the Cost of Density with Rents », SciencesPo Mimeo, 20182. Hill R.J., Syed I.A, « Hedonic price–rent ratios, user cost, and departures from equilibrium in the housing market», Regional Science and Urban Economics, pp 60-72, 2016

Keywords: Machine Learning; Rent profitability; Rent to price ratios; Web platform data (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2022-01-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ure
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