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From listings to all-tenant rents: a probabilistic model

Author

Listed:
  • Emanuel Nussli
  • Rachel Arulraj-Cordonier
  • Flurina Strasser
  • Marko Nanut Petrič
  • Morten Linnemann Bech
  • Antonio Pipino

Abstract

Rents are the largest component of the Consumer Price Index (CPI) in many countries, making accurate and timely measurements of rental price developments essential for inflation monitoring and policy decisions. Market (asking) rent indices are often available in near real-time and with high detail, but differ substantially from the rents paid by the overall tenant population, as typically measured in the CPI. This paper proposes a model to bridge the gap between asking and all-tenant rents. First, using rental-unit listings for Switzerland, we construct timely, granular, and high-frequency indices of asking rents. Second, using a probabilistic model that accounts for the duration of tenants' stays, we estimate all-tenant rents based on historical asking rents. Additionally, we incorporate rent changes during ongoing tenancies. For Switzerland, this corresponds to adjustments permitted under Swiss tenancy law in response to changes in the mortgage reference rate and inflation. This allows us to provide weekly, real-time, and highly disaggregated estimates of all-tenant rents, which are highly correlated with the official quarterly survey-based rental index in the Swiss CPI. Our approach provides a tool for timely rental price monitoring and forecasting that can be adapted for use in other countries.

Suggested Citation

  • Emanuel Nussli & Rachel Arulraj-Cordonier & Flurina Strasser & Marko Nanut Petrič & Morten Linnemann Bech & Antonio Pipino, 2025. "From listings to all-tenant rents: a probabilistic model," BIS Working Papers 1317, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:1317
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    References listed on IDEAS

    as
    1. Joël Vonlanthen, 2023. "Interest rates and real estate prices: a panel study," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-25, December.
    2. Brian Adams & Lara Loewenstein & Hugh Montag & Randal Verbrugge, 2024. "Disentangling Rent Index Differences: Data, Methods, and Scope," American Economic Review: Insights, American Economic Association, vol. 6(2), pages 230-245, June.
    3. Laurence M. Ball & Kyung Woong Koh, 2025. "Market Rents and CPI Shelter Inflation," NBER Working Papers 34113, National Bureau of Economic Research, Inc.
    4. Dhaene, J. & Denuit, M. & Goovaerts, M. J. & Kaas, R. & Vyncke, D., 2002. "The concept of comonotonicity in actuarial science and finance: theory," Insurance: Mathematics and Economics, Elsevier, vol. 31(1), pages 3-33, August.
    5. Whitney Newey & Kenneth West, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    6. Brent W. Ambrose & N. Edward Coulson & Jiro Yoshida, 2023. "Housing Rents and Inflation Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(4), pages 975-992, June.
    7. Michele Loberto & Andrea Luciani & Marco Pangallo, 2022. "What Do Online Listings Tell Us about the Housing Market?," International Journal of Central Banking, International Journal of Central Banking, vol. 18(4), pages 1-52, October.
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    Keywords

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    JEL classification:

    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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