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Housing Network Connectedness and Policy Spillovers: Evidence from a Time-Varying Parameter VAR Approach

Author

Listed:
  • Onur Polat

    (Hacettepe University, Informatics Institute 06800 Beytepe, Ankara, Turkiye)

  • Hardik A. Marfatia

    (Department of Economics, Northeastern Illinois University, 5500 N. St. Louis Ave, Chicago 60625, USA)

  • Christophe Andre

    (Economics Department, Organisation for Economic Co-operation and Development (OECD), 75775 Paris, Cedex 16, France)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

Abstract

This paper examines time-varying connectedness and volatility spillovers between housing markets and macroeconomic policy conditions, with particular emphasis on the role of housing deregulation. Using daily data from September 2007 to May 2026, we estimate a 15-node TVP-VAR network comprising housing volatility series for ten major U.S. metropolitan areas alongside five policy and financial indicators: the housing deregulation index, economic policy uncertainty (EPU), a spliced monetary policy proxy (Effective Federal Funds Rate/Krippner Shadow Short Rate), the Aruoba-Diebold-Scotti (ADS) business conditions index, and the 5-year breakeven inflation rate (T5YIE). Volatility inputs are filtered using a multivariate GJR-GARCH model augmented with time-varying skewness and kurtosis (GJRSK), and parameters are estimated within a Bayesian prior (BayesPrior) framework. The Total Connectedness Index reveals a counter-cyclical network topology, peaking during the 2008 financial crisis, the 2020 pandemic, and the 2024 monetary pivot. Pairwise decompositions reveal pronounced heterogeneity across cities: supply-inelastic coastal market, New York (peak spillover 45.5 percent), San Diego (48.8 percent), and San Francisco (39.8 percent), absorb the largest regulatory shocks, while deregulation also transmits persistently to monetary policy conditions (Shadow Short Rate positive in 87.7 percent of observations), real activity, and inflation expectations. These findings carry direct implications for macroprudential policy design and institutional portfolio risk management in the U.S. housing sector.

Suggested Citation

  • Onur Polat & Hardik A. Marfatia & Christophe Andre & Rangan Gupta, 2026. "Housing Network Connectedness and Policy Spillovers: Evidence from a Time-Varying Parameter VAR Approach," Working Papers 202630, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202630
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    References listed on IDEAS

    as
    1. Albert Saiz, 2010. "The Geographic Determinants of Housing Supply," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(3), pages 1253-1296.
    2. Chang-Tai Hsieh & Enrico Moretti, 2019. "Housing Constraints and Spatial Misallocation," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(2), pages 1-39, April.
    3. Greg Kaplan & Kurt Mitman & Giovanni L. Violante, 2020. "The Housing Boom and Bust: Model Meets Evidence," Journal of Political Economy, University of Chicago Press, vol. 128(9), pages 3285-3345.
    4. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    5. Woo Suk Lee & Eunseong Ma, 2025. "Housing market connectedness and transmission of monetary policy," Economic Inquiry, Western Economic Association International, vol. 63(3), pages 961-984, July.
    6. Rangan Gupta & Hardik A. Marfatia & Christian Pierdzioch & Afees A. Salisu, 2022. "Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty," The Journal of Real Estate Finance and Economics, Springer, vol. 64(4), pages 523-545, May.
    7. Loutskina, Elena & Strahan, Philip E., 2015. "Financial integration, housing, and economic volatility," Journal of Financial Economics, Elsevier, vol. 115(1), pages 25-41.
    8. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    9. Alejandro Justiniano & Giorgio E. Primiceri & Andrea Tambalotti, 2019. "Credit Supply and the Housing Boom," Journal of Political Economy, University of Chicago Press, vol. 127(3), pages 1317-1350.
    10. John V. Duca & John Muellbauer & Anthony Murphy, 2016. "How Mortgage Finance Reform Could Affect Housing," American Economic Review, American Economic Association, vol. 106(5), pages 620-624, May.
    11. Krippner, Leo, 2013. "Measuring the stance of monetary policy in zero lower bound environments," Economics Letters, Elsevier, vol. 118(1), pages 135-138.
    12. Petre Caraiani & Adrian C. Călin & Rangan Gupta, 2021. "Monetary policy and bubbles in US REITs," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 675-687, June.
    13. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    14. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    15. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    16. Sheng, Xin & Marfatia, Hardik A. & Gupta, Rangan & Ji, Qiang, 2021. "House price synchronization across the US states: The role of structural oil shocks," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    17. Ai, Hengjie & Li, Kai & Yang, Fang, 2020. "Financial intermediation and capital reallocation," Journal of Financial Economics, Elsevier, vol. 138(3), pages 663-686.
    18. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
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    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • 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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