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Nowcasting Transaction-Based House Price Indices Using Web-Scraped Listings and MIDAS Regression

Author

Listed:
  • Radoslaw Trojanek
  • Luke Hartigan
  • Norbert Pfeifer
  • Miriam Steurer

Abstract

Timely transaction-based residential property price indices are crucial for effective monetary and macroprudential policy, yet transaction-based data often suffer from significant reporting delays. Online property platforms, by contrast, provide list prices of properties in real-time. This paper examines whether immediately available online list prices can improve timely nowcasts of transaction price movements. Using 16 years of micro-level data from Warsaw and Poznan, we construct quality-adjusted monthly list-price and quarterly transaction-price indices using the hedonic rolling-time-dummy method. We find that list-price indices consistently lead transaction-price indices by one to two months, with the strongest relationship in Warsaw's larger, more liquid market. Building on this lead-lag relationship, we develop a Mixed Data Sampling (MIDAS) regression framework to nowcast quarterly transaction-price growth using monthly list-price data. Our preferred MIDAS specifications reduce one-quarter-ahead root mean square error by approximately 16-23 percent for Warsaw and 5-15 percent for Poznan relative to standard autoregressive benchmarks. The predictive advantage is greatest when incorporating list-price data from the first or second month of the quarter, as third-month data introduce forward-looking noise. Our results show that properly constructed list-price indices can play an important role to provide early housing market signals, potentially enhancing the timeliness of policy responses.

Suggested Citation

  • Radoslaw Trojanek & Luke Hartigan & Norbert Pfeifer & Miriam Steurer, 2025. "Nowcasting Transaction-Based House Price Indices Using Web-Scraped Listings and MIDAS Regression," CAMA Working Papers 2025-45, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2025-45
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    File URL: https://crawford.anu.edu.au/sites/default/files/2025-08/45_2025_Trojanek_Hartigan_Pfeifer_Steurer_1.pdf
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    References listed on IDEAS

    as
    1. Bajari, P. & Cen, Z. & Chernozhukov, V. & Manukonda, M. & Vijaykumar, S. & Wang, J. & Huerta, R. & Li, J. & Leng, L. & Monokroussos, G. & Wang, S., 2025. "Hedonic prices and quality adjusted price indices powered by AI," Journal of Econometrics, Elsevier, vol. 251(C).
    2. Alberto Cavallo & W. Erwin Diewert & Robert C. Feenstra & Robert Inklaar & Marcel P. Timmer, 2018. "Using Online Prices for Measuring Real Consumption across Countries," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 483-487, May.
    3. Elliot Anenberg & Steven Laufer, 2017. "A More Timely House Price Index," The Review of Economics and Statistics, MIT Press, vol. 99(4), pages 722-734, July.
    4. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    5. Choi, Chi-Young & Hansz, J. Andrew, 2021. "From banking integration to housing market integration - Evidence from the comovement of U.S. Metropolitan House Prices," Journal of Financial Stability, Elsevier, vol. 54(C).
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    Keywords

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    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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